IEEE Organizations related to Big Data Applications

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Conferences related to Big Data Applications

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Periodicals related to Big Data Applications

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Most published Xplore authors for Big Data Applications

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Xplore Articles related to Big Data Applications

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Trace-based method for big data memory characteristics research

2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2017

Big data has exacerbated the so-called “memory wall” problem. To study the memory characteristics of big data applications has become an important issue in the high end computing community. In this paper, we propose a trace-based method based on the trace files generated by simulators, which captures memory access information in different memory hierarchies and aggregates information to get memory ...


Love at First Sight: MonetDB/TensorFlow

2018 IEEE 34th International Conference on Data Engineering (ICDE), 2018

This talk first shows how an in-database machine learning system has been realised by a seamless integration of MonetDB (an open-source analytical columnar DBMS) and TensorFlow (an open-source machine learning library). Then we show with an example application of entity linking using neural embeddings the potential of this integration.


Towards Specification of a Software Architecture for Cross-Sectoral Big Data Applications

2019 IEEE World Congress on Services (SERVICES), 2019

The proliferation of Big Data applications puts pressure on improving and optimizing the handling of diverse datasets across different domains. Among several challenges, major difficulties arise in data-sensitive domains like banking, telecommunications, etc., where strict regulations make very difficult to upload and experiment with real data on external cloud resources. In addition, most Big Data research and development efforts aim ...


Rethinking Visual Analytics for Streaming Data Applications

IEEE Internet Computing, 2017

Visual analytics is entering a period of renewed growth due to a shift in focus from static to streaming data applications. In this article, the authors illustrate several challenges arising from this pivot and suggest potential avenues for future exploration.


Benefits of SDN for Big data applications

2017 14th International Conference on Smart Cities: Improving Quality of Life Using ICT & IoT (HONET-ICT), 2017

Big data applications depend on underlying networks that make the transfer of information possible. These networks may be real (conventional) or virtual (in case of services hosted in data centers). Either way, the responsibility of smooth execution of the application, despite increasing traffic volume, lies with the service provider. The service providers face many challenges with respect to providing a ...


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Educational Resources on Big Data Applications

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IEEE-USA E-Books

  • Trace-based method for big data memory characteristics research

    Big data has exacerbated the so-called “memory wall” problem. To study the memory characteristics of big data applications has become an important issue in the high end computing community. In this paper, we propose a trace-based method based on the trace files generated by simulators, which captures memory access information in different memory hierarchies and aggregates information to get memory performance statistics. Simulations were conducted to research the impact of cache size and hardware prefetch on big data applications, and our trace-based method was used to obtain the desired memory performance metrics. Experimental results show that big data benchmarks are less sensitive to cache size than traditional benchmarks, and hardware prefetching is effective in improving L2 cache hit rate. In terms of memory access address range, big data benchmarks have wider address range and the address distribution is more irregular than traditional benchmarks.

  • Love at First Sight: MonetDB/TensorFlow

    This talk first shows how an in-database machine learning system has been realised by a seamless integration of MonetDB (an open-source analytical columnar DBMS) and TensorFlow (an open-source machine learning library). Then we show with an example application of entity linking using neural embeddings the potential of this integration.

  • Towards Specification of a Software Architecture for Cross-Sectoral Big Data Applications

    The proliferation of Big Data applications puts pressure on improving and optimizing the handling of diverse datasets across different domains. Among several challenges, major difficulties arise in data-sensitive domains like banking, telecommunications, etc., where strict regulations make very difficult to upload and experiment with real data on external cloud resources. In addition, most Big Data research and development efforts aim to address the needs of IT experts, while Big Data analytics tools remain unavailable to non- expert users to a large extent. In this paper, we report on the work-in- progress carried out in the context of the H2020 project I-BiDaaS (Industrial- Driven Big Data as a Self-service Solution) which aims to address the above challenges. The project will design and develop a novel architecture stack that can be easily configured and adjusted to address cross-sectoral needs, helping to resolve data privacy barriers in sensitive domains, and at the same time being usable by non-experts. This paper discusses and motivates the need for Big Data as a self-service, reviews the relevant literature, and identifies gaps with respect to the challenges described above. We then present the I-BiDaaS paradigm for Big Data as a self-service, position it in the context of existing references, and report on initial work towards the conceptual specification of the I-BiDaaS software architecture.

  • Rethinking Visual Analytics for Streaming Data Applications

    Visual analytics is entering a period of renewed growth due to a shift in focus from static to streaming data applications. In this article, the authors illustrate several challenges arising from this pivot and suggest potential avenues for future exploration.

  • Benefits of SDN for Big data applications

    Big data applications depend on underlying networks that make the transfer of information possible. These networks may be real (conventional) or virtual (in case of services hosted in data centers). Either way, the responsibility of smooth execution of the application, despite increasing traffic volume, lies with the service provider. The service providers face many challenges with respect to providing a high quality of service. It is therefore in the best interest of the service providers that efficiency of the applications is increased. SDN has the potential to improve big data application performance. In this paper we have a look at the recent advancements in technology that helps improve big data applications using SDN and discuss our observations.

  • Efficient Non-Convex Graph Clustering for Big Data

    Big data analysis is a fundamental research topic with extensive technical obstacles yet to be overcome. Graph clustering has shown promise in addressing big data challenges by categorizing otherwise unlabeled data-thus giving them meaning. In this paper, we propose a set of non-convex programs, generally referred to as Hard and Soft Clustering programs, that rely on matrix factorization formulations for enhanced computational performance. Based on such formulations, we devise clustering algorithms that allow for large data analysis in a more efficient manner than traditional convex clustering techniques. Numerical results confirm the usefulness of the proposed algorithms for clustering purposes and reveal their potential for usage in big data applications.

  • Hash based optimization for faster access to inverted index

    Inverted Index is an important data structure in computer science. It is used to create a mapping between a word and the set of documents in which that word appears. Thus, it is used to store documents per word. Currently, the output of inverted indexing is stored haphazardly in a look up table. Hence traversing through the look up table for fetching indexes requires linear search. The time complexity of linear search is O(n) where n is the number of words whose inverted index has been stored. In this paper, a hash based optimization is proposed for storing the output of inverted index which can reduce the searching time complexity to O(1). Since inverted indexes are quite popular in big data applications like search engines, a MapReduce implementation of the proposed technique is also presented which can be easily implemented in a distributed environment.

  • Research on big data application in intelligent safety supervision

    Big date technology plays an important role in intelligent safety supervision. Based on overall framework of safety supervision big date platform, this issue demonstrates the process from the source to the end-user and the safety supervision big data application architecture. The data integration, analysis, processing and display technologies were analyzed to meet the requirements of intelligent safety supervision. Key technologies of big data will bring a new development opportunity for the construction of intelligent safety supervision platform.

  • A comparative study of relational database and key-value database for big data applications

    Nowadays, Demands of web scale are in increasing and growing rapidly. Mobile applications, web technologies, social media always generates unstructured data that had lead to the advent of various NoSQL databases. Therefore, Big data applications are necessary to have an efficient technology to collect these data. However, a relational database is the traditional database that always uses in many applications and still has more valuable to play a significant role in the current information system. The main characteristics of NoSQL databases are schema-free, no relationship, no need to join as a relational database. The business organization expects that NoSQL database has better performance than a relational database. In This paper, we aim to compare the performance of Redis, which is a key-value database, one kind of NoSQL database, and MariaDB, which is a popular relational database. We designed a set of experiments with a large amount of data and compared the efficiency of the insert, update, delete and select transactions from various aspects on the same dataset. We measure the processing time of each transaction to evaluate the comparison. The results have shown that Redis has better runtime performance for insert, delete, update transaction under a specific condition or complex queries. MariaDB still is good for some conditions especially when we have a small data. Our study can help to choose a database that will be suitable for the real world applications because relational databases and NoSQL databases have different strengths and weakness.

  • A Dynamic Migration Method for Big Data in Cloud

    Big data applications store data sets through sharing data center under the Cloud computing environment, but the need of data set in big data applications is dynamic change over time. In face of multiple data centers, such applications meet new challenges in data migration which mainly include how to how to reduce the number of network access, how to reduce the overall time consumption, and how to improve the efficiency by the time of balancing the global load in the migration process. Facing these challenges, we first build the problem model and descript the dynamic migration method, then solve the global time consumption of data migration, the number of network access and global load balancing these three parameters. Finally, do the cloud computing simulation experiment under the Cloudsim experiment platform. The result shows that the proposed method makes the task completion time reduced by 10% and the data transmission time accounts for the roportion of the total time is reduced. When the amount of data sets is increase, the proportion can reduces to 50% or less. Network access number lower than Zipf and reached stable, in global load, the variance of the node's store space closed to zero.



Standards related to Big Data Applications

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Jobs related to Big Data Applications

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