The anatomy of big data computing

Kune, Raghavendra; Konugurthi, Pramod Kumar; Agarwal, Arun; Chillarige, Raghavendra Rao; Buyya, Rajkumar

doi:10.1002/spe.2374

Cited by 134 publications

(69 citation statements)

References 29 publications

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“…[8] focuses on the analysis of this type of information and shows the Apache Hadoop Project and MapReduce as an approximation. In [40] the authors also discuss the evolution of the Big Data computing, its taxonomy, the technologies, etc., but they progress and integrate it with the concept of cloud. The proposal presented in this paper is based on an Ontological Web-Trading Model, as another approach to support the Vs of the Big Data mentioned above.…”

Section: Related Workmentioning

confidence: 99%

“…The content of these files is stated starting in line 14. The attributes (lines 20-38) check whether the node has each of the modules defined in the TKRS to include the corresponding attribute (for example, the QueryModule is found in lines [23][24][25][26][27][28][29]. The class constructor (lines 40-60) is where the attributes defined earlier are initialized and so on.…”

Section: Modeling the Trading-based Knowledge Representation System Cmentioning

confidence: 99%

“…In this files, configuration and initialization operations (in the class constructor) of all its parameters (class attributes) are performed based on the specific values in the architecture design. Figure 17 shows the content of one of these Java files, which corresponds to the implementation of the corresponding node defined in Figure 14 (extracted from it and placed next to this [15][16][17][18][19][20][21][22][23][24][25], the aforementioned attributes are initialized with the values shown by means of the graphics tool, and so on. This is repeated for nodes 2 and 3 of the system architecture.…”

Section: System Configurationmentioning

confidence: 99%

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Modeling Big data‐based systems through ontological trading

et al. 2017

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SUMMARYOne of the great challenges the information society faces is dealing with the huge amount of information generated and handled daily on the Internet. Today, progress in Big Data proposals attempt to solve this problem, but there are certain limitations to information search and retrieval due basically to the large volumes handled, the heterogeneity of the information and its dispersion among a multitude of sources. In this article, a formal framework is defined to facilitate the design and development of an Environmental Management Information System which works with an heterogeneous and large amount of data. Nevertheless, this framework can be applied to other information systems that work with Big Data, since it does not depend on the type of data and can be utilized in other domains. The framework is based on an Ontological Web-Trading Model (OntoTrader) which follows Model-Driven Engineering and OntologyDriven Engineering guidelines to separate the system architecture from its implementation. The proposal is accompanied by a case study, SOLERES-KRS, an Environmental Knowledge Representation System designed and developed using Software Agents and Multi-Agent Systems.

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Section: Related Workmentioning

confidence: 99%

Section: Modeling the Trading-based Knowledge Representation System Cmentioning

confidence: 99%

Section: System Configurationmentioning

confidence: 99%

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Modeling Big data‐based systems through ontological trading

et al. 2017

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“…The amount of textual and multimedia data has grown considerably large in recent years because of the growth of social networking, healthcare applications, surveillance systems, earth observation sensors, and so on. This huge volume of data in the world has created a new field in data processing called as Big Data , which refers to an emerging data science paradigm of multidimensional information mining for scientific discovery and business analytics over large‐scale scalable infrastructure . Big Data handles massive amounts of data collected over time, which is otherwise difficult task to analyze and handle using common database management tools .…”

Section: Introductionmentioning

confidence: 99%

XHAMI – extended HDFS and MapReduce interface for Big Data image processing applications in cloud computing environments

Kune¹,

Konugurthi²,

Agarwal

et al. 2016

Softw Pract Exp

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Summary Hadoop distributed file system (HDFS) and MapReduce model have become popular technologies for large‐scale data organization and analysis. Existing model of data organization and processing in Hadoop using HDFS and MapReduce are ideally tailored for search and data parallel applications, for which there is no need of data dependency with its neighboring/adjacent data. However, many scientific applications such as image mining, data mining, knowledge data mining, and satellite image processing are dependent on adjacent data for processing and analysis. In this paper, we identify the requirements of the overlapped data organization and propose a two‐phase extension to HDFS and MapReduce programming model, called XHAMI, to address them. The extended interfaces are presented as APIs and implemented in the context of image processing application domain. We demonstrated effectiveness of XHAMI through case studies of image processing functions along with the results. Although XHAMI has little overhead in data storage and input/output operations, it greatly enhances the system performance and simplifies the application development process. Our proposed system, XHAMI, works without any changes for the existing MapReduce models and can be utilized by many applications where there is a requirement of overlapped data. Copyright © 2016 John Wiley & Sons, Ltd.

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“…Traditionally, business intelligence has been understood as a set of methodologies, applications and technologies used to transform data into information and then information into a personal profile of clients that is generated into structured data to serve different areas of business enterprise [6]. Therefore, Big Data will aid to develop better procedures that allow (BI) tools to be used to gather information, such as [7]: i) Process and analyse volumes of information; ii) Increase the universe of data to consider when decisionmaking: and inherent historical data of the company, to incorporate data from external sources ; iii) Provide an immediate response to the continued provision of real-time data of the devices and the possibilities of interconnections between devices; iv) Working with structures of complex and heterogeneous data: logs, emails, conversations, locations, voice, etc. ; v) and lastly, to Isolate from the physical constraints of storage and process by making use of scalable solutions and high availability at a competitive prices.…”

Section: Introductionmentioning

confidence: 99%

Pentaho and Jaspersoft: A Comparative Study of Business Intelligence Open Source Tools Processing Big Data to Evaluate Performances

Parra¹,

Mohammad²,

Syed³

et al. 2016

ijacsa

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Abstract-Regardless of the recent growth in the use of "BigData" and "Business Intelligence" (BI) tools, little research has been undertaken about the implications involved. Analytical tools affect the development and sustainability of a company, as evaluating clientele needs to advance in the competitive market is critical. With the advancement of the population, processing large amounts of data has become too cumbersome for companies. At some stage in a company's lifecycle, all companies need to create new and better data processing systems that improve their decision-making processes. Companies use BI Results to collect data that is drawn from interpretations grouped from cues in the data set BI information system that helps organisations with activities that give them the advantage in a competitive market. However, many organizations establish such systems, without conducting a preliminary analysis of the needs and wants of a company, or without determining the benefits and targets that they aim to achieve with the implementation. They rarely measure the large costs associated with the implementation blowout of such applications, which results in these impulsive solutions that are unfinished or too complex and unfeasible, in other words unsustainable even if implemented. BI open source tools are specific tools that solve this issue for organizations in need, with data storage and management. This paper compares two of the best positioned BI open source tools in the market: Pentaho and Jaspersoft, processing big data through six different sized databases, especially focussing on their Extract Transform and Load (ETL) and Reporting processes by measuring their performances using Computer Algebra Systems (CAS). The ETL experimental analysis results clearly show that Jaspersoft BI has an increment of CPU time in the process of data over Pentaho BI, which is represented by an average of 42.28% in performance metrics over the six databases. Meanwhile, Pentaho BI had a marked increment of the CPU time in the process of data over Jaspersoft evidenced by the reporting analysis outcomes with an average of 43.12% over six databases that prove the point of this study. This study is a guiding reference for many researchers and those IT professionals who support the conveniences of Big Data processing, and the implementation of BI open source tool based on their needs.

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The anatomy of big data computing

Abstract: Abstract-Advances in information technology and its widespread growth in several areas

Cited by 134 publications

References 29 publications

Modeling Big data‐based systems through ontological trading

Modeling Big data‐based systems through ontological trading

XHAMI – extended HDFS and MapReduce interface for Big Data image processing applications in cloud computing environments

Pentaho and Jaspersoft: A Comparative Study of Business Intelligence Open Source Tools Processing Big Data to Evaluate Performances

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