RonPub - OJBD: articles

Open Journal of Big Data (OJBD)
OJBD, an open access and peer-reviewed online journal, publishes original and creative research results on Big Data. OJBD distributes its articles under the open access model. All articles of OJBD are fully open access and online available to readers free of charge. There is no restriction on the length of the papers. Accepted manuscripts are published online immediately.

Publisher:	RonPub UG (haftungsbeschränkt), Lübeck, Germany
Contact:	OJBD Editorial Office ojbd@ronpub.com
ISSN:	2365-029X
Call for Papers:	txt_UTF-8 txt_ASCII pdf

Open Journal of Big Data (OJBD)
OJBD, an open access and peer-reviewed online journal, publishes original and creative research results on Big Data. OJBD distributes its articles under the open access model. All articles of OJBD are fully open access and online available to readers free of charge. There is no restriction on the length of the papers. Accepted manuscripts are published online immediately.

Publisher:	RonPub UG (haftungsbeschränkt), Lübeck, Germany
Contact:	OJBD Editorial Office ojbd@ronpub.com
ISSN:	2365-029X
Call for Papers:	txt_UTF-8 txt_ASCII pdf

Aims & Scope Statistics Author Guidelines Editorial Board Articles SI Special Issues Publication Fees Indexing Submit

Aims & Scope

Big Data research is expected to be the hottest topic for the next five years. We shall have solid plans and regular meetings to ensure that our journal attracts the best papers from reputable researchers to support our mission continuously. Our objectives are as follows:

Disseminate the emerging techniques, technologies and services associated with Big Data.
Offer empirical evidence and approaches to demonstrate contributions made by Big Data.
Offer recommendations to research and enterprise communities that use Big Data as a solution for their work.
Offer guidelines and strategic directions in the way that Big Data research should progress.

We will seek recommendations and practices that can be successfully delivered to other disciplines such as healthcare, finance, education and science, providing us quality papers centered on Big Data and whose lessons learned will be transferable across disciplines to encourage inter-disciplinary research and funding activities essential for progressive research and development. We will cover extensive studies to ensure that the research and enterprise communities can take our recommendations, guidelines and best practices, which will make real positive impacts to their services and projects. We will ensure that key lessons taken from our journal can be very useful to communities. By blending workshops and calls for papers in our journal, we will ensure that our articles are of the highest caliber and can demonstrate added values and benefits to the people adopting our recommendations. We will ensure all submitters understand and use our recommendations, so that their citations and adoptions of our key lessons will keep our quality high.

Our journal has an advantage over the competing journals in Big Data as follows. First, steps involved in Big Data development should be reproducible to allow organizations to follow. Some articles in competing journals are very theoretical, making reproduction difficult. Second, all demonstrated deliveries in our journal should be easy to use, and provide real added value to technology-adopting organizations beyond just technical implementations. Unlike some articles in competing journals, whose deliveries are hard to understand and don’t consider technical or organizational adoption. We also encourage industrial partners to provide their latest developments, success stories (empirical) and best practices (quantitative and qualitative) to ensure our journal articles have the edge over others.

The Open Journal of Big Data (OJBD) welcomes high-quality and scholarly papers, which include new methodologies, processes, case studies, proofs-of-concept, scientific demonstrations, industrial applications and adoption. The journal covers a wide range of topics including Big Data science, frameworks, analytics, visualizations, recommendations and data-intensive research. The OJBD presents the current challenges faced by Big Data adoption and implementation, and recommends ways, techniques, services and technologies that can resolve existing challenges and improve on the current practices. We focus on how Big Data can make huge positive impacts to different disciplines in addition to IT, which include healthcare, finance, education, physical science, biological science, earth science, business & management, information systems, social sciences and law. There are eight major topics as follows:

Techniques, algorithms and innovative methods of processing Big Data (or Big datasets) that achieve performance, accuracy and low-costs.
Design, implementation, evaluation and services related to Big Data, including the development process, use cases, experiments and associated simulations.
Systems and applications developed by Big Data and descriptions of how Big Data can be used in disciplines such as bioinformatics, finance, education, natural science, weather science, life science, physics, astronomy, law and social science.
Security, privacy, trust, data ownership, legal challenges, business models, information systems, social implications, social network analyses and social science related to Big Data.
Consolidation of existing technologies (databases, web, mobile, HPC) and how to integrate them in Big Data such as SOA Big Data, data mining, machine learning, HPC Big Data and cloud storage.
Recommendations, emerging technologies and techniques associated with Big Data such as mobile Big Data, standards, multi-clouds and internet of things.
Data analysis, analytics and visualization, including GPU techniques, new algorithms and methods showing how to achieve significant improvements from existing methods.
Surveys, case studies, frameworks and user evaluations involved with qualitative, quantitative and/or computational research methods.

Author Guidelines

Publication Criteria

Publication Criteria provides important information for authors to prepare their manuscripts with a high possibility of being accepted.

Manuscript Preparation

Please prepare your manuscripts using the manuscript template of the journal. It is available for download as word doc docx and latex version zip. The template describes the format and structure of manuscripts and other necessary information for preparing manuscripts. Manuscripts should be written in English. There is no restriction on the length of manuscripts.

Submission

Authors submit their manuscripts following the information on the submit page. Authors first submit their manuscripts in PDF format. Once a manuscript is accepted, the author then submits the revised manuscript as a PDF file and a word file or latex folder (with all the material necessary to generate the PDF file). The work described in the submitted manuscript must be previously unpublished; it is not under consideration for publication anywhere else.

Authors are welcome to suggest qualified reviewers for their papers, but this is not mandatory. If the author wants to do so, please provide the name, affiliations and e-mail addresses for all suggested reviewers.

Manuscript Status

After submission of manuscripts, authors will receive an email to confirm receipt of manuscripts. Subsequent enquiries concerning paper progress should be sent to the email address of the journal.

Review Procedure

OJBD is committed to enforcing a rigorous peer-review process. All manuscripts submitted for publication in OJBD are strictly and thoroughly peer-reviewed. When a manuscript is submitted, the editor-in-chief assigns it to an appropriate editor who will be in charge of the review process of the manuscript. The editor first suggests potential reviewers and then organizes the peer-reviewing herself/himself or entrusts it to the editor office. For each manuscript, typically three review reports will be collected. The editor and the editor-in-chief evaluate the manuscript itself and the review reports and make an accept/revision/reject decision. Authors will be informed with the decision and reviewing results within 6-8 weeks on average after the manuscript submission. In the case of revision, authors are required to perform an adequate revision to address the concerns from evaluation reports. A second round of peer-review will be performed if necessary.

Accepted manuscripts are published online immediately.

Copyrights

Authors publishing with RonPub open journals retain the copyright to their work.

All articles published by RonPub is fully open access and online available to readers free of charge. RonPub publishes all open access articles under the Creative Commons Attribution License, which permits unrestricted use, distribution and reproduction freely, provided that the original work is properly cited.

Digital Archiving Policy

Our publications have been archived and permanently-preserved in the German National Library. The publications, which are archived in the German National Library, are not only long-term preserved but also accessible in the future, because the German National Library ensures that digital data saved in the old formats can be viewed and used on current computer systems in the same way they were on the original systems which are long obsolete. Further measures will be taken if necessary. Furthermore, we also encourage our authors to self-archive their articles published on the website of RonPub.

Publication Ethics Statement

In order to ensure the publishing quality and the reputation of the journal, it is important that all parties involved in the act of publishing adhere to the standards of the publishing ethical behaviour. To verify the originality of submissions, we use Plagiarism Detection Tools, like Anti-Plagiarism, PaperRater, Viper, to check the content of manuscripts submitted to our journals against existing publications.

Our journal follows the Code of Conduct of the Committee on Publication Ethics (COPE), and deals with the cases of misconduct according to the COPE Flowcharts.

Articles of OJBD

Open Access

Translation of Array-based Loop Programs to Optimized SQL-based Distributed Programs

Md Hasanuzzaman Noor, Leonidas Fegaras, Tanvir Ahmed Khan, Tanzima Sultana

Open Journal of Big Data (OJBD), 6(1), Pages 1-25, 2022, Downloads: 2486

Abstract: Many data analysis programs are often expressed in terms of array operations in sequential loops. However, these programs do not scale very well to large amounts of data that cannot fit in the memory of a single computer and they have to be rewritten to work on Big Data analysis platforms, such as Map-Reduce and Spark. We present a novel framework, called SQLgen, that automatically translates sequential loops on arrays to distributed data-parallel programs, specifically Spark SQL programs. We further extend this framework by introducing OSQLgen, which automatically parallelizes array-based loop programs to distributed data-parallel programs on block arrays. At first, our framework translates the sequential loops on arrays to monoid comprehensions and then to Spark SQL. For SQLgen, the SQL is over coordinate arrays while for OSQLgen, it is over block arrays. As block arrays are more compact than coordinate arrays, computations on block matrices are significantly faster than on arrays in the coordinate format. Since not all array-based loops can be translated to SQL on block arrays, we focus on certain patterns of loops that match an algebraic structure known as a semiring. Many linear algebra operations, such as matrix multiplication required in many machine learning algorithms, as well as many graph programs that are equivalent to a semiring can be translated to distributed data-parallel programs on block arrays using OSQLgen, thus giving us a substantial performance gain. Finally, to evaluate our framework, we compare the performance of OSQLgen with GraphX, GraphFrames, MLlib, and hand-written Spark SQL programs on coordinate and block arrays on various real-world problems.

BibTex:

    @Article{OJBD_2022v6i1n01_Noor,
        title     = {Translation of Array-based Loop Programs to Optimized SQL-based Distributed Programs},
        author    = {Md Hasanuzzaman Noor and
                     Leonidas Fegaras and
                     Tanvir Ahmed Khan and
                     Tanzima Sultana},
        journal   = {Open Journal of Big Data (OJBD)},
        issn      = {2365-029X},
        year      = {2022},
        volume    = {6},
        number    = {1},
        pages     = {1--25},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-2022041308230855567393},
        urn       = {urn:nbn:de:101:1-2022041308230855567393},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {Many data analysis programs are often expressed in terms of array operations in sequential loops. However, these programs do not scale very well to large amounts of data that cannot fit in the memory of a single computer and they have to be rewritten to work on Big Data analysis platforms, such as Map-Reduce and Spark. We present a novel framework, called SQLgen, that automatically translates sequential loops on arrays to distributed data-parallel programs, specifically Spark SQL programs. We further extend this framework by introducing OSQLgen, which automatically parallelizes array-based loop programs to distributed data-parallel programs on block arrays. At first, our framework translates the sequential loops on arrays to monoid comprehensions and then to Spark SQL. For SQLgen, the SQL is over coordinate arrays while for OSQLgen, it is over block arrays. As block arrays are more compact than coordinate arrays, computations on block matrices are significantly faster than on arrays in the coordinate format. Since not all array-based loops can be translated to SQL on block arrays, we focus on certain patterns of loops that match an algebraic structure known as a semiring. Many linear algebra operations, such as matrix multiplication required in many machine learning algorithms, as well as many graph programs that are equivalent to a semiring can be translated to distributed data-parallel programs on block arrays using OSQLgen, thus giving us a substantial performance gain. Finally, to evaluate our framework, we compare the performance of OSQLgen with GraphX, GraphFrames, MLlib, and hand-written Spark SQL programs on coordinate and block arrays on various real-world problems.}
    }

Open Access

A SIEM Architecture for Advanced Anomaly Detection

Tim Laue, Timo Klecker, Carsten Kleiner, Kai-Oliver Detken

Open Journal of Big Data (OJBD), 6(1), Pages 26-42, 2022, Downloads: 23661

Abstract: Dramatic increases in the number of cyber security attacks and breaches toward businesses and organizations have been experienced in recent years. The negative impacts of these breaches not only cause the stealing and compromising of sensitive information, malfunctioning of network devices, disruption of everyday operations, financial damage to the attacked business or organization itself, but also may navigate to peer businesses/organizations in the same industry. Therefore, prevention and early detection of these attacks play a significant role in the continuity of operations in IT-dependent organizations. At the same time detection of various types of attacks has become extremely difficult as attacks get more sophisticated, distributed and enabled by Artificial Intelligence (AI). Detection and handling of these attacks require sophisticated intrusion detection systems which run on powerful hardware and are administered by highly experienced security staff. Yet, these resources are costly to employ, especially for small and medium-sized enterprises (SMEs). To address these issues, we developed an architecture -within the GLACIER project- that can be realized as an in-house operated Security Information Event Management (SIEM) system for SMEs. It is affordable for SMEs as it is solely based on free and open-source components and thus does not require any licensing fees. Moreover, it is a Self-Contained System (SCS) and does not require too much management effort. It requires short configuration and learning phases after which it can be self-contained as long as the monitored infrastructure is stable (apart from a reaction to the generated alerts which may be outsourced to a service provider in SMEs, if necessary). Another main benefit of this system is to supply data to advanced detection algorithms, such as multidimensional analysis algorithms, in addition to traditional SIEMspecific tasks like data collection, normalization, enrichment, and storage. It supports the application of novel methods to detect security-related anomalies. The most distinct feature of this system that differentiates it from similar solutions in the market is its user feedback capability. Detected anomalies are displayed in a Graphical User Interface (GUI) to the security staff who are allowed to give feedback for anomalies. Subsequently, this feedback is utilized to fine-tune the anomaly detection algorithm. In addition, this GUI also provides access to network actors for quick incident responses. The system in general is suitable for both Information Technology (IT) and Operational Technology (OT) environments, while the detection algorithm must be specifically trained for each of these environments individually.

BibTex:

    @Article{OJBD_2022v6i1n02_Laue,
        title     = {A SIEM Architecture for Advanced Anomaly Detection},
        author    = {Tim Laue and
                     Timo Klecker and
                     Carsten Kleiner and
                     Kai-Oliver Detken},
        journal   = {Open Journal of Big Data (OJBD)},
        issn      = {2365-029X},
        year      = {2022},
        volume    = {6},
        number    = {1},
        pages     = {26--42},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-2022070319330522943055},
        urn       = {urn:nbn:de:101:1-2022070319330522943055},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {Dramatic increases in the number of cyber security attacks and breaches toward businesses and organizations have been experienced in recent years. The negative impacts of these breaches not only cause the stealing and compromising of sensitive information, malfunctioning of network devices, disruption of everyday operations, financial damage to the attacked business or organization itself, but also may navigate to peer businesses/organizations in the same industry. Therefore, prevention and early detection of these attacks play a significant role in the continuity of operations in IT-dependent organizations. At the same time detection of various types of attacks has become extremely difficult as attacks get more sophisticated, distributed and enabled by Artificial Intelligence (AI). Detection and handling of these attacks require sophisticated intrusion detection systems which run on powerful hardware and are administered by highly experienced security staff. Yet, these resources are costly to employ, especially for small and medium-sized enterprises (SMEs). To address these issues, we developed an architecture -within the GLACIER project- that can be realized as an in-house operated Security Information Event Management (SIEM) system for SMEs. It is affordable for SMEs as it is solely based on free and open-source components and thus does not require any licensing fees. Moreover, it is a Self-Contained System (SCS) and does not require too much management effort. It requires short configuration and learning phases after which it can be self-contained as long as the monitored infrastructure is stable (apart from a reaction to the generated alerts which may be outsourced to a service provider in SMEs, if necessary). Another main benefit of this system is to supply data to advanced detection algorithms, such as multidimensional analysis algorithms, in addition to traditional SIEMspecific tasks like data collection, normalization, enrichment, and storage. It supports the application of novel methods to detect security-related anomalies. The most distinct feature of this system that differentiates it from similar solutions in the market is its user feedback capability. Detected anomalies are displayed in a Graphical User Interface (GUI) to the security staff who are allowed to give feedback for anomalies. Subsequently, this feedback is utilized to fine-tune the anomaly detection algorithm. In addition, this GUI also provides access to network actors for quick incident responses. The system in general is suitable for both Information Technology (IT) and Operational Technology (OT) environments, while the detection algorithm must be specifically trained for each of these environments individually.}
    }

Open Access

Sparse and Dense Linear Algebra for Machine Learning on Parallel-RDBMS Using SQL

Dennis Marten, Holger Meyer, Daniel Dietrich, Andreas Heuer

Open Journal of Big Data (OJBD), 5(1), Pages 1-34, 2019, Downloads: 5700, Citations: 4

Abstract: While computational modelling gets more complex and more accurate, its calculation costs have been increasing alike. However, working on big data environments usually involves several steps of massive unfiltered data transmission. In this paper, we continue our work on the PArADISE framework, which enables privacy aware distributed computation of big data scenarios, and present a study on how linear algebra operations can be calculated over parallel relational database systems using SQL. We investigate the ways to improve the computation performance of algebra operations over relational databases and show how using database techniques impacts the computation performance like the use of indexes, choice of schema, query formulation and others. We study the dense and sparse problems of linear algebra over relational databases and show that especially sparse problems can be efficiently computed using SQL. Furthermore, we present a simple but universal technique to improve intra-operator parallelism for linear algebra operations in order to support the parallel computation of big data.

BibTex:

    @Article{OJBD_2019v5i1n01_Marten,
        title     = {Sparse and Dense Linear Algebra for Machine Learning on Parallel-RDBMS Using SQL},
        author    = {Dennis Marten and
                     Holger Meyer and
                     Daniel Dietrich and
                     Andreas Heuer},
        journal   = {Open Journal of Big Data (OJBD)},
        issn      = {2365-029X},
        year      = {2019},
        volume    = {5},
        number    = {1},
        pages     = {1--34},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-2018122318341069172957},
        urn       = {urn:nbn:de:101:1-2018122318341069172957},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {While computational modelling gets more complex and more accurate, its calculation costs have been increasing alike. However, working on big data environments usually involves several steps of massive unfiltered data transmission. In this paper, we continue our work on the PArADISE framework, which enables privacy aware distributed computation of big data scenarios, and present a study on how linear algebra operations can be calculated over parallel relational database systems using SQL. We investigate the ways to improve the computation performance of algebra operations over relational databases and show how using database techniques impacts the computation performance like the use of indexes, choice of schema, query formulation and others. We study the dense and sparse problems of linear algebra over relational databases and show that especially sparse problems can be efficiently computed using SQL. Furthermore, we present a simple but universal technique to improve intra-operator parallelism for linear algebra operations in order to support the parallel computation of big data.}
    }

Open Access

Compile-Time Query Optimization for Big Data Analytics

Leonidas Fegaras

Open Journal of Big Data (OJBD), 5(1), Pages 35-61, 2019, Downloads: 5665, Citations: 4

Abstract: Many emerging programming environments for large-scale data analysis, such as Map-Reduce, Spark, and Flink, provide Scala-based APIs that consist of powerful higher-order operations that ease the development of complex data analysis applications. However, despite the simplicity of these APIs, many programmers prefer to use declarative languages, such as Hive and Spark SQL, to code their distributed applications. Unfortunately, most current data analysis query languages are based on the relational model and cannot effectively capture the rich data types and computations required for complex data analysis applications. Furthermore, these query languages are not well-integrated with the host programming language, as they are based on an incompatible data model. To address these shortcomings, we introduce a new query language for data-intensive scalable computing that is deeply embedded in Scala, called DIQL, and a query optimization framework that optimizes and translates DIQL queries to byte code at compile-time. In contrast to other query languages, our query embedding eliminates impedance mismatch as any Scala code can be seamlessly mixed with SQL-like syntax, without having to add any special declaration. DIQL supports nested collections and hierarchical data and allows query nesting at any place in a query. With DIQL, programmers can express complex data analysis tasks, such as PageRank and matrix factorization, using SQL-like syntax exclusively. The DIQL query optimizer uses algebraic transformations to derive all possible joins in a query, including those hidden across deeply nested queries, thus unnesting nested queries of any form and any number of nesting levels. The optimizer also uses general transformations to push down predicates before joins and to prune unneeded data across operations. DIQL has been implemented on three Big Data platforms, Apache Spark, Apache Flink, and Twitter's Cascading/Scalding, and has been shown to have competitive performance relative to Spark DataFrames and Spark SQL for some complex queries. This paper extends our previous work on embedded data-intensive query languages by describing the complete details of the formal framework and the query translation and optimization processes, and by providing more experimental results that give further evidence of the performance of our system.

BibTex:

    @Article{OJBD_2019v5i1n02_Fegaras,
        title     = {Compile-Time Query Optimization for Big Data Analytics},
        author    = {Leonidas Fegaras},
        journal   = {Open Journal of Big Data (OJBD)},
        issn      = {2365-029X},
        year      = {2019},
        volume    = {5},
        number    = {1},
        pages     = {35--61},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-2019041419330955160405},
        urn       = {urn:nbn:de:101:1-2019041419330955160405},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {Many emerging programming environments for large-scale data analysis, such as Map-Reduce, Spark, and Flink, provide Scala-based APIs that consist of powerful higher-order operations that ease the development of complex data analysis applications. However, despite the simplicity of these APIs, many programmers prefer to use declarative languages, such as Hive and Spark SQL, to code their distributed applications. Unfortunately, most current data analysis query languages are based on the relational model and cannot effectively capture the rich data types and computations required for complex data analysis applications. Furthermore, these query languages are not well-integrated with the host programming language, as they are based on an incompatible data model. To address these shortcomings, we introduce a new query language for data-intensive scalable computing that is deeply embedded in Scala, called DIQL, and a query optimization framework that optimizes and translates DIQL queries to byte code at compile-time. In contrast to other query languages, our query embedding eliminates impedance mismatch as any Scala code can be seamlessly mixed with SQL-like syntax, without having to add any special  declaration. DIQL supports nested collections and hierarchical data and allows query nesting at any place in a query. With DIQL, programmers can express complex data analysis tasks, such as PageRank and matrix factorization, using SQL-like syntax exclusively. The DIQL query optimizer uses algebraic transformations to derive all possible joins in a query, including those hidden across deeply nested queries, thus unnesting nested queries of any form and any number of nesting levels. The optimizer also uses general transformations to push down predicates before joins and to prune unneeded data across operations. DIQL has been implemented on three Big Data platforms, Apache Spark, Apache Flink, and Twitter's Cascading/Scalding, and has been shown to have competitive performance relative to Spark DataFrames and Spark SQL for some complex queries. This paper extends our previous work on embedded data-intensive query languages by describing the complete details of the formal framework and the query translation and optimization processes, and by providing more experimental results that give further evidence of the performance of our system.}
    }

Open Access

Modelling Patterns in Continuous Streams of Data

Ricardo Jesus, Mario Antunes, Diogo Gomes, Rui L. Aguiar

Open Journal of Big Data (OJBD), 4(1), Pages 1-13, 2018, Downloads: 4607, Citations: 3

Abstract: The untapped source of information, extracted from the increasing number of sensors, can be explored to improve and optimize several systems. Yet, hand in hand with this growth goes the increasing difficulty to manage and organize all this new information. The lack of a standard context representation scheme is one of the main struggles in this research area. Conventional methods for extracting knowledge from data rely on a standard representation or a priori relation, which may not be feasible for IoT and M2M scenarios. With this in mind we propose a stream characterization model in order to provide the foundations for a novel stream similarity metric. Complementing previous work on context organization, we aim to provide an automatic stream organizational model without enforcing specific representations. In this paper we extend our work on stream characterization and devise a novel similarity method.

BibTex:

    @Article{OJBD_2018v4i1n01_Jesus,
        title     = {Modelling Patterns in Continuous Streams of Data},
        author    = {Ricardo Jesus and
                     Mario Antunes and
                     Diogo Gomes and
                     Rui L. Aguiar},
        journal   = {Open Journal of Big Data (OJBD)},
        issn      = {2365-029X},
        year      = {2018},
        volume    = {4},
        number    = {1},
        pages     = {1--13},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-201801234777},
        urn       = {urn:nbn:de:101:1-201801234777},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {The untapped source of information, extracted from the increasing number of sensors, can be explored to improve and optimize several systems. Yet, hand in hand with this growth goes the increasing difficulty to manage and organize all this new information. The lack of a standard context representation scheme is one of the main struggles in this research area. Conventional methods for extracting knowledge from data rely on a standard representation or a priori relation, which may not be feasible for IoT and M2M scenarios. With this in mind we propose a stream characterization model in order to provide the foundations for a novel stream similarity metric. Complementing previous work on context organization, we aim to provide an automatic stream organizational model without enforcing specific representations. In this paper we extend our work on stream characterization and devise a novel similarity method.}
    }

Open Access

Operation of Modular Smart Grid Applications Interacting through a Distributed Middleware

Stephan Cejka, Albin Frischenschlager, Mario Faschang, Mark Stefan, Konrad Diwold

Open Journal of Big Data (OJBD), 4(1), Pages 14-29, 2018, Downloads: 5447, Citations: 5

Abstract: IoT-functionality can broaden the scope of distribution system automation in terms of functionality and communication. However, it also poses risks regarding resource consumption and security. This article presents a field approved IoT-enabled smart grid middleware, which allows for flexible deployment and management of applications within smart grid operation. In the first part of the work, the resource consumption of the middleware is analyzed and current memory bottlenecks are identified. The bottlenecks can be resolved by introducing a new entity that allows to dynamically load multiple applications within one JVM. The performance was experimentally tested and the results suggest that its application can significantly reduce the applications' memory footprint on the physical device. The second part of the study identifies and discusses potential security threats, with a focus on attacks stemming from malicious software applications within the framework. In order to prevent such attacks a proxy based prevention mechanism is developed and demonstrated.

BibTex:

    @Article{OJBD_2018v4i1n02_Cejka,
        title     = {Operation of Modular Smart Grid Applications Interacting through a Distributed Middleware},
        author    = {Stephan Cejka and
                     Albin Frischenschlager and
                     Mario Faschang and
                     Mark Stefan and
                     Konrad Diwold},
        journal   = {Open Journal of Big Data (OJBD)},
        issn      = {2365-029X},
        year      = {2018},
        volume    = {4},
        number    = {1},
        pages     = {14--29},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-201801212419},
        urn       = {urn:nbn:de:101:1-201801212419},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {IoT-functionality can broaden the scope of distribution system automation in terms of functionality and communication. However, it also poses risks regarding resource consumption and security. This article presents a field approved IoT-enabled smart grid middleware, which allows for flexible deployment and management of applications within smart grid operation. In the first part of the work, the resource consumption of the middleware is analyzed and current memory bottlenecks are identified. The bottlenecks can be resolved by introducing a new entity that allows to dynamically load multiple applications within one JVM. The performance was experimentally tested and the results suggest that its application can significantly reduce the applications' memory footprint on the physical device. The second part of the study identifies and discusses potential security threats, with a focus on attacks stemming from malicious software applications within the framework. In order to prevent such attacks a proxy based prevention mechanism is developed and demonstrated.}
    }

Open Access

Cloud-Scale Entity Resolution: Current State and Open Challenges

Xiao Chen, Eike Schallehn, Gunter Saake

Open Journal of Big Data (OJBD), 4(1), Pages 30-51, 2018, Downloads: 5458, Citations: 18

Abstract: Entity resolution (ER) is a process to identify records in information systems, which refer to the same real-world entity. Because in the two recent decades the data volume has grown so large, parallel techniques are called upon to satisfy the ER requirements of high performance and scalability. The development of parallel ER has reached a relatively prosperous stage, and has found its way into several applications. In this work, we first comprehensively survey the state of the art of parallel ER approaches. From the comprehensive overview, we then extract the classification criteria of parallel ER, classify and compare these approaches based on these criteria. Finally, we identify open research questions and challenges and discuss potential solutions and further research potentials in this field.

BibTex:

    @Article{OJBD_2018v4i1n03_Chen,
        title     = {Cloud-Scale Entity Resolution: Current State and Open Challenges},
        author    = {Xiao Chen and
                     Eike Schallehn and
                     Gunter Saake},
        journal   = {Open Journal of Big Data (OJBD)},
        issn      = {2365-029X},
        year      = {2018},
        volume    = {4},
        number    = {1},
        pages     = {30--51},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-201804155766},
        urn       = {urn:nbn:de:101:1-201804155766},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {Entity resolution (ER) is a process to identify records in information systems, which refer to the same real-world entity. Because in the two recent decades the data volume has grown so large, parallel techniques are called upon to satisfy the ER requirements of high performance and scalability. The development of parallel ER has reached a relatively prosperous stage, and has found its way into several applications. In this work, we first comprehensively survey the state of the art of parallel ER approaches. From the comprehensive overview, we then extract the classification criteria of parallel ER, classify and compare these approaches based on these criteria. Finally, we identify open research questions and challenges and discuss potential solutions and further research potentials in this field.}
    }

Open Access

Technology Selection for Big Data and Analytical Applications

Denis Lehmann, David Fekete, Gottfried Vossen

Open Journal of Big Data (OJBD), 3(1), Pages 1-25, 2017, Downloads: 4392, Citations: 2

Abstract: The term Big Data has become pervasive in recent years, as smart phones, televisions, washing machines, refrigerators, smart meters, diverse sensors, eyeglasses, and even clothes connect to the Internet. However, their generated data is essentially worthless without appropriate data analytics that utilizes information retrieval, statistics, as well as various other techniques. As Big Data is commonly too big for a single person or institution to investigate, appropriate tools are being used that go way beyond a traditional data warehouse and that have been developed in recent years. Unfortunately, there is no single solution but a large variety of different tools, each of which with distinct functionalities, properties and characteristics. Especially small and medium-sized companies have a hard time to keep track, as this requires time, skills, money, and specific knowledge that, in combination, result in high entrance barriers for Big Data utilization. This paper aims to reduce these barriers by explaining and structuring different classes of technologies and the basic criteria for proper technology selection. It proposes a framework that guides especially small and mid-sized companies through a suitable selection process that can serve as a basis for further advances.

BibTex:

    @Article{OJBD_2017v3n01_Lehmann,
        title     = {Technology Selection for Big Data and Analytical Applications},
        author    = {Denis Lehmann and
                     David Fekete and
                     Gottfried Vossen},
        journal   = {Open Journal of Big Data (OJBD)},
        issn      = {2365-029X},
        year      = {2017},
        volume    = {3},
        number    = {1},
        pages     = {1--25},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-201711266876},
        urn       = {urn:nbn:de:101:1-201711266876},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {The term Big Data has become pervasive in recent years, as smart phones, televisions, washing machines, refrigerators, smart meters, diverse sensors, eyeglasses, and even clothes connect to the Internet. However, their generated data is essentially worthless without appropriate data analytics that utilizes information retrieval, statistics, as well as various other techniques. As Big Data is commonly too big for a single person or institution to investigate, appropriate tools are being used that go way beyond a traditional data warehouse and that have been developed in recent years. Unfortunately, there is no single solution but a large variety of different tools, each of which with distinct functionalities, properties and characteristics. Especially small and medium-sized companies have a hard time to keep track, as this requires time, skills, money, and specific knowledge that, in combination, result in high entrance barriers for Big Data utilization. This paper aims to reduce these barriers by explaining and structuring different classes of technologies and the basic criteria for proper technology selection. It proposes a framework that guides especially small and mid-sized companies through a suitable selection process that can serve as a basis for further advances.}
    }

Open Access

Combining Process Guidance and Industrial Feedback for Successfully Deploying Big Data Projects

Christophe Ponsard, Mounir Touzani, Annick Majchrowski

Open Journal of Big Data (OJBD), 3(1), Pages 26-41, 2017, Downloads: 5505, Citations: 7

Abstract: Companies are faced with the challenge of handling increasing amounts of digital data to run or improve their business. Although a large set of technical solutions are available to manage such Big Data, many companies lack the maturity to manage that kind of projects, which results in a high failure rate. This paper aims at providing better process guidance for a successful deployment of Big Data projects. Our approach is based on the combination of a set of methodological bricks documented in the literature from early data mining projects to nowadays. It is complemented by learned lessons from pilots conducted in different areas (IT, health, space, food industry) with a focus on two pilots giving a concrete vision of how to drive the implementation with emphasis on the identification of values, the definition of a relevant strategy, the use of an Agile follow-up and a progressive rise in maturity.

BibTex:

    @Article{OJBD_2017v3i1n02_Ponsard,
        title     = {Combining Process Guidance and Industrial Feedback for Successfully Deploying Big Data Projects},
        author    = {Christophe Ponsard and
                     Mounir Touzani and
                     Annick Majchrowski},
        journal   = {Open Journal of Big Data (OJBD)},
        issn      = {2365-029X},
        year      = {2017},
        volume    = {3},
        number    = {1},
        pages     = {26--41},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-201712245446},
        urn       = {urn:nbn:de:101:1-201712245446},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {Companies are faced with the challenge of handling increasing amounts of digital data to run or improve their business. Although a large set of technical solutions are available to manage such Big Data, many companies lack the maturity to manage that kind of projects, which results in a high failure rate. This paper aims at providing better process guidance for a successful deployment of Big Data projects. Our approach is based on the combination of a set of methodological bricks documented in the literature from early data mining projects to nowadays. It is complemented by learned lessons from pilots conducted in different areas (IT, health, space, food industry) with a focus on two pilots giving a concrete vision of how to drive the implementation with emphasis on the identification of values, the definition of a relevant strategy, the use of an Agile follow-up and a progressive rise in maturity.}
    }

Open Access

Conformance of Social Media as Barometer of Public Engagement

Songchun Moon

Open Journal of Big Data (OJBD), 2(1), Pages 1-10, 2016, Downloads: 5903

Abstract: There have been continuously a number of expectations: Social media may play a role of indicator that shows the degree of engagement and preference of choices of users toward music or movies. However, finding appropriate software tools in the market to verify this sort of expectation is too costly and complicated in their natures, and this causes a number of difficulties to attempt technical experimentation. A convenient and easy tool to facilitate such experimentation was developed in this study and was used successfully for performing various measurements with regard to user engagement in music and movies.

BibTex:

    @Article{OJBD_2016v2i101_Moon,
        title     = {Conformance of Social Media as Barometer of Public Engagement},
        author    = {Songchun Moon},
        journal   = {Open Journal of Big Data (OJBD)},
        issn      = {2365-029X},
        year      = {2016},
        volume    = {2},
        number    = {1},
        pages     = {1--10},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-201705194393},
        urn       = {urn:nbn:de:101:1-201705194393},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {There have been continuously a number of expectations: Social media may play a role of indicator that shows the degree of engagement and preference of choices of users toward music or movies. However, finding appropriate software tools in the market to verify this sort of expectation is too costly and complicated in their natures, and this causes a number of difficulties to attempt technical experimentation. A convenient and easy tool to facilitate such experimentation was developed in this study and was used successfully for performing various measurements with regard to user engagement in music and movies.}
    }

Open Access

Constructing Large-Scale Semantic Web Indices for the Six RDF Collation Orders

Sven Groppe, Dennis Heinrich, Christopher Blochwitz, Thilo Pionteck

Open Journal of Big Data (OJBD), 2(1), Pages 11-25, 2016, Downloads: 5967, Citations: 1

Abstract: The Semantic Web community collects masses of valuable and publicly available RDF data in order to drive the success story of the Semantic Web. Efficient processing of these datasets requires their indexing. Semantic Web indices make use of the simple data model of RDF: The basic concept of RDF is the triple, which hence has only 6 different collation orders. On the one hand having 6 collation orders indexed fast merge joins (consuming the sorted input of the indices) can be applied as much as possible during query processing. On the other hand constructing the indices for 6 different collation orders is very time-consuming for large-scale datasets. Hence the focus of this paper is the efficient Semantic Web index construction for large-scale datasets on today's multi-core computers. We complete our discussion with a comprehensive performance evaluation, where our approach efficiently constructs the indices of over 1 billion triples of real world data.

BibTex:

    @Article{OJBD_2016v2i1n02_Groppe,
        title     = {Constructing Large-Scale Semantic Web Indices for the Six RDF Collation Orders},
        author    = {Sven Groppe and
                     Dennis Heinrich and
                     Christopher Blochwitz and
                     Thilo Pionteck},
        journal   = {Open Journal of Big Data (OJBD)},
        issn      = {2365-029X},
        year      = {2016},
        volume    = {2},
        number    = {1},
        pages     = {11--25},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-201705194418},
        urn       = {urn:nbn:de:101:1-201705194418},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {The Semantic Web community collects masses of valuable and publicly available RDF data in order to drive the success story of the Semantic Web. Efficient processing of these datasets requires their indexing. Semantic Web indices make use of the simple data model of RDF: The basic concept of RDF is the triple, which hence has only 6 different collation orders. On the one hand having 6 collation orders indexed fast merge joins (consuming the sorted input of the indices) can be applied as much as possible during query processing. On the other hand constructing the indices for 6 different collation orders is very time-consuming for large-scale datasets. Hence the focus of this paper is the efficient Semantic Web index construction for large-scale datasets on today's multi-core computers. We complete our discussion with a comprehensive performance evaluation, where our approach efficiently constructs the indices of over 1 billion triples of real world data.}
    }

Open Access

New Areas of Contributions and New Addition of Security

Victor Chang

Open Journal of Big Data (OJBD), 2(1), Pages 26-28, 2016, Downloads: 4048

Abstract: Open Journal of Big Data (OJBD) (www.ronpub.com/ojbd) is an open access journal, which addresses the aspects of Big Data, including new methodologies, processes, case studies, poofs-of-concept, scientific demonstrations, industrial applications and adoption. This editorial presents two articles published in the first issue of the second volume of OJBD. The first article is about the investigation of social media for the public engagement. The second article looks into large-scale semantic web indices for six RDF collation orders. OJBD has an increasingly improved reputation thanks to the support of research communities. We will set up the Second International Conference on Internet of Things, Big Data and Security (IoTBDS 2017), in Porto, Portugal, between 24 and 26 April 2017. OJBD is published by RonPub (www.ronpub.com), which is an academic publisher of online, open access, peer-reviewed journals.

BibTex:

    @Article{OJBD_2016v2i1n03e_Chang,
        title     = {New Areas of Contributions and New Addition of Security},
        author    = {Victor Chang},
        journal   = {Open Journal of Big Data (OJBD)},
        issn      = {2365-029X},
        year      = {2016},
        volume    = {2},
        number    = {1},
        pages     = {26--28},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-201705194405},
        urn       = {urn:nbn:de:101:1-201705194405},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {Open Journal of Big Data (OJBD) (www.ronpub.com/ojbd) is an open access journal, which addresses the aspects of Big Data, including new methodologies, processes, case studies, poofs-of-concept, scientific demonstrations, industrial applications and adoption. This editorial presents two articles published in the first issue of the second volume of OJBD. The first article is about the investigation of social media for the public engagement. The second article looks into large-scale semantic web indices for six RDF collation orders. OJBD has an increasingly improved reputation thanks to the support of research communities. We will set up the Second International Conference on Internet of Things, Big Data and Security (IoTBDS 2017), in Porto, Portugal, between 24 and 26 April 2017. OJBD is published by RonPub (www.ronpub.com), which is an academic publisher of online, open access, peer-reviewed journals.}
    }

Open Access

Big Data in the Cloud: A Survey

Pedro Caldeira Neves, Jorge Bernardino

Open Journal of Big Data (OJBD), 1(2), Pages 1-18, 2015, Downloads: 12384, Citations: 14

Abstract: Big Data has become a hot topic across several business areas requiring the storage and processing of huge volumes of data. Cloud computing leverages Big Data by providing high storage and processing capabilities and enables corporations to consume resources in a pay-as-you-go model making clouds the optimal environment for storing and processing huge quantities of data. By using virtualized resources, Cloud can scale very easily, be highly available and provide massive storage capacity and processing power. This paper surveys existing databases models to store and process Big Data within a Cloud environment. Particularly, we detail the following traditional NoSQL databases: BigTable, Cassandra, DynamoDB, HBase, Hypertable, and MongoDB. The MapReduce framework and its developments Apache Spark, HaLoop, Twister, and other alternatives such as Apache Giraph, GraphLab, Pregel and MapD - a novel platform that uses GPU processing to accelerate Big Data processing - are also analyzed. Finally, we present two case studies that demonstrate the successful use of Big Data within Cloud environments and the challenges that must be addressed in the future.

BibTex:

    @Article{OJBD_2015v1i2n02_Neves,
        title     = {Big Data in the Cloud: A Survey},
        author    = {Pedro Caldeira Neves and
                     Jorge Bernardino},
        journal   = {Open Journal of Big Data (OJBD)},
        issn      = {2365-029X},
        year      = {2015},
        volume    = {1},
        number    = {2},
        pages     = {1--18},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-201705194365},
        urn       = {urn:nbn:de:101:1-201705194365},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {Big Data has become a hot topic across several business areas requiring the storage and processing of huge volumes of data. Cloud computing leverages Big Data by providing high storage and processing capabilities and enables corporations to consume resources in a pay-as-you-go model making clouds the optimal environment for storing and processing huge quantities of data. By using virtualized resources, Cloud can scale very easily, be highly available and provide massive storage capacity and processing power. This paper surveys existing databases models to store and process Big Data within a Cloud environment. Particularly, we detail the following traditional NoSQL databases: BigTable, Cassandra, DynamoDB, HBase, Hypertable, and MongoDB. The MapReduce framework and its developments Apache Spark, HaLoop, Twister, and other alternatives such as Apache Giraph, GraphLab, Pregel and MapD - a novel platform that uses GPU processing to accelerate Big Data processing - are also analyzed. Finally, we present two case studies that demonstrate the successful use of Big Data within Cloud environments and the challenges that must be addressed in the future.}
    }

Open Access

Statistical Machine Learning in Brain State Classification using EEG Data

Yuezhe Li, Yuchou Chang, Hong Lin

Open Journal of Big Data (OJBD), 1(2), Pages 19-33, 2015, Downloads: 10556, Citations: 4

Abstract: In this article, we discuss how to use a variety of machine learning methods, e.g. tree bagging, random forest, boost, support vector machine, and Gaussian mixture model, for building classifiers for electroencephalogram (EEG) data, which is collected from different brain states on different subjects. Also, we discuss how training data size influences misclassification rate. Moreover, the number of subjects that contributes to the training data affects misclassification rate. Furthermore, we discuss how sample entropy contributes to building a classifier. Our results show that classification based on sample entropy give the smallest misclassification rate. Moreover, two data sets were collected from one channel and seven channels respectively. The classification results of each data set show that the more channels we use, the less misclassification we have. Our results show that it is promising to build a self-adaptive classification system by using EEG data to distinguish idle from active state.

BibTex:

    @Article{OJBD_2015v1i2n03_YuehzeLi,
        title     = {Statistical Machine Learning in Brain State Classification using EEG Data},
        author    = {Yuezhe Li and
                     Yuchou Chang and
                     Hong Lin},
        journal   = {Open Journal of Big Data (OJBD)},
        issn      = {2365-029X},
        year      = {2015},
        volume    = {1},
        number    = {2},
        pages     = {19--33},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-201705194354},
        urn       = {urn:nbn:de:101:1-201705194354},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {In this article, we discuss how to use a variety of machine learning methods, e.g. tree bagging, random forest, boost, support vector machine, and Gaussian mixture model, for building classifiers for electroencephalogram (EEG) data, which is collected from different brain states on different subjects. Also, we discuss how training data size influences misclassification rate. Moreover, the number of subjects that contributes to the training data affects misclassification rate. Furthermore, we discuss how sample entropy contributes to building a classifier. Our results show that classification based on sample entropy give the smallest misclassification rate. Moreover, two data sets were collected from one channel and seven channels respectively. The classification results of each data set show that the more channels we use, the less misclassification we have. Our results show that it is promising to build a self-adaptive classification system by using EEG data to distinguish idle from active state.}
    }

Open Access

Data Transfers in Hadoop: A Comparative Study

Ujjal Marjit, Kumar Sharma, Puspendu Mandal

Open Journal of Big Data (OJBD), 1(2), Pages 34-46, 2015, Downloads: 13223, Citations: 4

Abstract: Hadoop is an open source framework for processing large amounts of data in distributed computing environment. It plays an important role in processing and analyzing the Big Data. This framework is used for storing data on large clusters of commodity hardware. Data input and output to and from Hadoop is an indispensable action for any data processing job. At present, many tools have been evolved for importing and exporting Data in Hadoop. In this article, some commonly used tools for importing and exporting data have been emphasized. Moreover, a state-of-the-art comparative study among the various tools has been made. With this study, it has been decided that where to use one tool over the other with emphasis on the data transfer to and from Hadoop system. This article also discusses about how Hadoop handles backup and disaster recovery along with some open research questions in terms of Big Data transfer when dealing with cloud-based services.

BibTex:

    @Article{OJBD_2015v1i2n04_UjjalMarjit,
        title     = {Data Transfers in Hadoop: A Comparative Study},
        author    = {Ujjal Marjit and
                     Kumar Sharma and
                     Puspendu Mandal},
        journal   = {Open Journal of Big Data (OJBD)},
        issn      = {2365-029X},
        year      = {2015},
        volume    = {1},
        number    = {2},
        pages     = {34--46},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-201705194373},
        urn       = {urn:nbn:de:101:1-201705194373},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {Hadoop is an open source framework for processing large amounts of data in distributed computing environment. It plays an important role in processing and analyzing the Big Data. This framework is used for storing data on large clusters of commodity hardware. Data input and output to and from Hadoop is an indispensable action for any data processing job. At present, many tools have been evolved for importing and exporting Data in Hadoop. In this article, some commonly used tools for importing and exporting data have been emphasized. Moreover, a state-of-the-art comparative study among the various tools has been made. With this study, it has been decided that where to use one tool over the other with emphasis on the data transfer to and from Hadoop system. This article also discusses about how Hadoop handles backup and disaster recovery along with some open research questions in terms of Big Data transfer when dealing with cloud-based services.}
    }

Open Access

Epilogue: Summary and Outlook

Victor Chang, Muthu Ramachandran, Robert John Walters, Gary B. Wills

Open Journal of Big Data (OJBD), 1(2), Pages 47-50, 2015, Downloads: 6210

Abstract: Open Journal of Big Data (OJBD) is an open access journal addressing aspects of Big Data, including new methodologies, processes, case studies, poofs-of-concept, scientific demonstrations, industrial applications and adoption. This editorial presents three articles in the second issue. The first paper is on Big Data in the Cloud. The second paper is on Statistical Machine Learning in Brain State Classification using EEG Data. The third article is on Data Transfers in Hadoop. OJBD has a rising reputation thanks to the support of research communities, which has helped us set up the First International Conference on Internet of Things and Big Data (IoTBD 2016), in Rome, Italy, between 23 and 25 April 2016. OJBD is published by RonPub (www.ronpub.com), which is an academic publisher of online, open access, peer-reviewed journals.

BibTex:

    @Article{OJBD_2015v1i2n05e_Chang,
        title     = {Epilogue: Summary and Outlook},
        author    = {Victor Chang and
                     Muthu Ramachandran and
                     Robert John Walters and
                     Gary B. Wills},
        journal   = {Open Journal of Big Data (OJBD)},
        issn      = {2365-029X},
        year      = {2015},
        volume    = {1},
        number    = {2},
        pages     = {47--50},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-201705194383},
        urn       = {urn:nbn:de:101:1-201705194383},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {Open Journal of Big Data (OJBD) is an open access journal addressing aspects of Big Data, including new methodologies, processes, case studies, poofs-of-concept, scientific demonstrations, industrial applications and adoption. This editorial presents three articles in the second issue. The first paper is on Big Data in the Cloud. The second paper is on Statistical Machine Learning in Brain State Classification using EEG Data. The third article is on Data Transfers in Hadoop. OJBD has a rising reputation thanks to the support of research communities, which has helped us set up the First International Conference on Internet of Things and Big Data (IoTBD 2016), in Rome, Italy, between 23 and 25 April 2016. OJBD is published by RonPub (www.ronpub.com), which is an academic publisher of online, open access, peer-reviewed journals.}
    }

Open Access

Introductory Editorial

Victor Chang, Muthu Ramachandran, Robert John Walters, Gary B. Wills

Open Journal of Big Data (OJBD), 1(1), Pages 1-3, 2015, Downloads: 5896

Abstract: The Open Journal of Big Data is a new open access journal published by RonPub, and RonPub is an academic publisher of online, open access, peer-reviewed journals. OJBD addresses aspects of Big Data, including new methodologies, processes, case studies, poofs-of-concept, scientific demonstrations, industrial applications and adoption. This editorial presents the two articles in this first issue. The first paper is on An Efficient Approach for Cost Optimization of the Movement of Big Data, which mainly focuses on the challenge of moving big data from one data center to other.The second paper is on Cognitive Spam Recognition Using Hadoop and Multicast-Update, which describes a method to make machines cognitively label spam using Machine Learning and the Naive Bayesian approach. OJBD has a rising reputation thanks to the support of research communities, which help us set up the First International Conference on Internet of Things and Big Data 2016 (IoTBD 2016), in Rome, Italy, between 23 and 25 April 2016.

BibTex:

    @Article{OJBD_2015v1i1n01_Chang,
        title     = {Introductory Editorial},
        author    = {Victor Chang and
                     Muthu Ramachandran and
                     Robert John Walters and
                     Gary B. Wills},
        journal   = {Open Journal of Big Data (OJBD)},
        issn      = {2365-029X},
        year      = {2015},
        volume    = {1},
        number    = {1},
        pages     = {1--3},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-201705194326},
        urn       = {urn:nbn:de:101:1-201705194326},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {The Open Journal of Big Data is a new open access journal published by RonPub, and RonPub is an academic publisher of online, open access, peer-reviewed journals. OJBD addresses aspects of Big Data, including new methodologies, processes, case studies, poofs-of-concept, scientific demonstrations, industrial applications and adoption. This editorial presents the two articles in this first issue. The first paper is on An Efficient Approach for Cost Optimization of the Movement of Big Data, which mainly focuses on the challenge of moving big data from one data center to other.The second paper is on Cognitive Spam Recognition Using Hadoop and Multicast-Update, which describes a method to make machines cognitively label spam using Machine Learning and the Naive Bayesian approach. OJBD has a rising reputation thanks to the support of research communities, which help us set up the First International Conference on Internet of Things and Big Data 2016 (IoTBD 2016), in Rome, Italy, between 23 and 25 April 2016. }
    }

Open Access

An Efficient Approach for Cost Optimization of the Movement of Big Data

Prasad Teli, Manoj V. Thomas, K. Chandrasekaran

Open Journal of Big Data (OJBD), 1(1), Pages 4-15, 2015, Downloads: 10634, Citations: 11

Abstract: With the emergence of cloud computing, Big Data has caught the attention of many researchers in the area of cloud computing. As the Volume, Velocity and Variety (3 Vs) of big data are growing exponentially, dealing with them is a big challenge, especially in the cloud environment. Looking at the current trend of the IT sector, cloud computing is mainly used by the service providers to host their applications. A lot of research has been done to improve the network utilization of WAN (Wide Area Network) and it has achieved considerable success over the traditional LAN (Local Area Network) techniques. While dealing with this issue, the major questions of data movement such as from where to where this big data will be moved and also how the data will be moved, have been overlooked. As various applications generating the big data are hosted in geographically distributed data centers, they individually collect large volume of data in the form of application data as well as the logs. This paper mainly focuses on the challenge of moving big data from one data center to other. We provide an efficient algorithm for the optimization of cost in the movement of the big data from one data center to another for offline environment. This approach uses the graph model for data centers in the cloud and results show that the adopted mechanism provides a better solution to minimize the cost for data movement.

BibTex:

    @Article{OJBD_2015v1i1n02_Teli,
        title     = {An Efficient Approach for Cost Optimization of the Movement of Big Data},
        author    = {Prasad Teli and
                     Manoj V. Thomas and
                     K. Chandrasekaran},
        journal   = {Open Journal of Big Data (OJBD)},
        issn      = {2365-029X},
        year      = {2015},
        volume    = {1},
        number    = {1},
        pages     = {4--15},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-201705194335},
        urn       = {urn:nbn:de:101:1-201705194335},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {With the emergence of cloud computing, Big Data has caught the attention of many researchers in the area of cloud computing. As the Volume, Velocity and Variety (3 Vs) of big data are growing exponentially, dealing with them is a big challenge, especially in the cloud environment. Looking at the current trend of the IT sector, cloud computing is mainly used by the service providers to host their applications. A lot of research has been done to improve the network utilization of WAN (Wide Area Network) and it has achieved considerable success over the traditional LAN (Local Area Network) techniques. While dealing with this issue, the major questions of data movement such as from where to where this big data will be moved and also how the data will be moved, have been overlooked. As various applications generating the big data are hosted in geographically distributed data centers, they individually collect large volume of data in the form of application data as well as the logs. This paper mainly focuses on the challenge of moving big data from one data center to other. We provide an efficient algorithm for the optimization of cost in the movement of the big data from one data center to another for offline environment. This approach uses the graph model for data centers in the cloud and results show that the adopted mechanism provides a better solution to minimize the cost for data movement.}
    }

Open Access

Cognitive Spam Recognition Using Hadoop and Multicast-Update

Mukund YR, Sunil Sandeep Nayak, K. Chandrasekaran

Open Journal of Big Data (OJBD), 1(1), Pages 16-28, 2015, Downloads: 9913, Citations: 2

Abstract: In today's world of exponentially growing technology, spam is a very common issue faced by users on the internet. Spam not only hinders the performance of a network, but it also wastes space and time, and causes general irritation and presents a multitude of dangers - of viruses, malware, spyware and consequent system failure, identity theft, and other cyber criminal activity. In this context, cognition provides us with a method to help improve the performance of the distributed system. It enables the system to learn what it is supposed to do for different input types as different classifications are made over time and this learning helps it increase its accuracy as time passes. Each system on its own can only do so much learning, because of the limited sample set of inputs that it gets to process. However, in a network, we can make sure that every system knows the different kinds of inputs available and learns what it is supposed to do with a better success rate. Thus, distribution and combination of this cognition across different components of the network leads to an overall improvement in the performance of the system. In this paper, we describe a method to make machines cognitively label spam using Machine Learning and the Naive Bayesian approach. We also present two possible methods of implementation - using a MapReduce Framework (hadoop), and also using messages coupled with a multicast-send based network - with their own subtypes, and the pros and cons of each. We finally present a comparative analysis of the two main methods and provide a basic idea about the usefulness of the two in various different scenarios.

BibTex:

    @Article{OJBD_2015v1i1n03_YR,
        title     = {Cognitive Spam Recognition Using Hadoop and Multicast-Update},
        author    = {Mukund YR and
                     Sunil Sandeep Nayak and
                     K. Chandrasekaran},
        journal   = {Open Journal of Big Data (OJBD)},
        issn      = {2365-029X},
        year      = {2015},
        volume    = {1},
        number    = {1},
        pages     = {16--28},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-201705194340},
        urn       = {urn:nbn:de:101:1-201705194340},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {In today's world of exponentially growing technology, spam is a very common issue faced by users on the internet. Spam not only hinders the performance of a network, but it also wastes space and time, and causes general irritation and presents a multitude of dangers - of viruses, malware, spyware and consequent system failure, identity theft, and other cyber criminal activity. In this context, cognition provides us with a method to help improve the performance of the distributed system. It enables the system to learn what it is supposed to do for different input types as different classifications are made over time and this learning helps it increase its accuracy as time passes. Each system on its own can only do so much learning, because of the limited sample set of inputs that it gets to process. However, in a network, we can make sure that every system knows the different kinds of inputs available and learns what it is supposed to do with a better success rate. Thus, distribution and combination of this cognition across different components of the network leads to an overall improvement in the performance of the system. In this paper, we describe a method to make machines cognitively label spam using Machine Learning and the Naive Bayesian approach. We also present two possible methods of implementation - using a MapReduce Framework (hadoop), and also using messages coupled with a multicast-send based network - with their own subtypes, and the pros and cons of each. We finally present a comparative analysis of the two main methods and provide a basic idea about the usefulness of the two in various different scenarios.}
    }

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	Omar Abdul-Rahman, National Institute of Informatics, Japan
	Mithun Balakrishna, Lymba Corporation, USA
	Victor Chang, Teesside University, UK
	Amin Hosseinian Far, University of Northampton, UK
	Chung-Sheng Li, PwC Labs, USA
	Mark Schueler, University of Southampton, UK
	Yun Wan, University of Houston, Victoria, USA
	Gary Wills, University of Southampton, UK

	Saad Alahmari, Shaqra University, Saudi Arabia
	Reinhold Behringer, Knorr Bremse GmbH, Germany
	Clinton Chee, Altair, Australia
	Anne James, Coventry University, UK
	Pethuru Raj, IBM Global Cloud Center of Excellence, India
	Luis M. Vaquero, Dyson, London, UK
	Junbo Wang, University of Aizu, Japan
	Neil Yuwen Yen, University of Aizu, Japan

	Mitra Arami, PARDIS Ltd, London, UK
	K. Chandrasekaran, National Institute of Technology Karnataka, India
	Dickson K.W. Chiu, University of Hong Kong, Hong Kong
	Verena Kantere, National Technical University of Athens, Greece
	Muthu Ramachandran, Leeds Beckett University, UK
	Luis Veiga, Inesc-id / Ist, Portugal
	Tomasz Wiktorski, University of Stavanger, Norway, UK
	Fan Zhang, MIT, USA

	Omar Abdul-Rahman, National Institute of Informatics, Japan
	Mithun Balakrishna, Lymba Corporation, USA
	Victor Chang, Teesside University, UK
	Amin Hosseinian Far, University of Northampton, UK
	Chung-Sheng Li, PwC Labs, USA
	Mark Schueler, University of Southampton, UK
	Yun Wan, University of Houston, Victoria, USA
	Gary Wills, University of Southampton, UK

	Saad Alahmari, Shaqra University, Saudi Arabia
	Reinhold Behringer, Knorr Bremse GmbH, Germany
	Clinton Chee, Altair, Australia
	Anne James, Coventry University, UK
	Pethuru Raj, IBM Global Cloud Center of Excellence, India
	Luis M. Vaquero, Dyson, London, UK
	Junbo Wang, University of Aizu, Japan
	Neil Yuwen Yen, University of Aizu, Japan

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