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2015 31st IEEE International Conference on Data Engineering Workshops

13-17 April 2015

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Displaying Results 1 - 25 of 63
  • Hub page

    Publication Year: 2015, Page(s): 1
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  • Session list

    Publication Year: 2015, Page(s): 1
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  • Table of contents

    Publication Year: 2015, Page(s):1 - 7
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  • Brief author index

    Publication Year: 2015, Page(s):1 - 4
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  • Detailed author index

    Publication Year: 2015, Page(s):1 - 29
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  • The end of indexes

    Publication Year: 2015, Page(s): 1
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  • About CP

    Publication Year: 2015, Page(s): 1
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  • Frequently asked questions

    Publication Year: 2015, Page(s):1 - 6
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  • First Workshop on Cloud Data Management (CloudDM) — Joint Workshop of Data Management in the Cloud (DMC) and CloudDB

    Publication Year: 2015, Page(s):1 - 3
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  • A biased guide to building a web-scale OLTP service

    Publication Year: 2015, Page(s): 4
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (97 KB)

    Many OLTP deployments in the web-scale industry start with a single-node open-source DBMS, add automation to manage many instances of the DBMS and then work with an open-source community to make things better via a large number of small improvements. The end result is far from perfect but frequently good enough to allow a business to focus on something other than the DBMS. The result is also frequ... View full abstract»

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  • The service contract between cloud provider and consumer: Consistency properties

    Publication Year: 2015, Page(s): 5
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (113 KB)

    There is a wide diversity of choices for a consumer who needs a data store to hold the information for a cloud-hosted application. Some stores are run as services by the cloud provider, others may be deployed and administered directly by the cloud consumer in infrastructure from the cloud provider. Some stores use a document model, others are relational, or graph, or they may even offer just a key... View full abstract»

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  • Metadata-as-a-Service

    Publication Year: 2015, Page(s):6 - 9
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (238 KB) | HTML iconHTML

    We present a vision of a technology and domain agnostic service that will store metadata that describes properties of the diverse data sets in an enterprise (or across several enterprises), and spread among heterogenous stores, such as relational databases, data warehouses, NoSQL or NewSQL cloud storage platforms, etc. The Metadata-as-a-Service will allow search over the metadata, so users and app... View full abstract»

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  • Dynamic resource management in a MapReduce-style platform for fast data processing

    Publication Year: 2015, Page(s):10 - 13
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (229 KB) | HTML iconHTML

    There is a recent interest in building MapReduce-style platforms for fast data processing, such as MapReduce online [2] and Muppet [5]. In this paper, we highlight the need for dynamic load management in a distributed data stream processing system and present Enorm, a MapReduce-style data stream processing platform with the focus on techniques to achieve dynamic resource management, i.e. the abili... View full abstract»

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  • Spotgres - parallel data analytics on Spot Instances

    Publication Year: 2015, Page(s):14 - 21
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1097 KB) | HTML iconHTML

    Market-based IaaS offers such as Amazon's EC2 Spot Instances represent a cost-efficient way to operate a cluster. Compared to traditional IaaS offers which follow a fixed pricing scheme, the per hour price of Spot Instances changes dynamically, whereas the Spot price is often significantly less when compared to On-demand and even the Reserved Instances. When deploying a Parallel Data-Processing En... View full abstract»

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  • AIR: Adaptive Index Replacement in Hadoop

    Publication Year: 2015, Page(s):22 - 29
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (329 KB) | HTML iconHTML

    The Hadoop Distributed Filesystem has become the de-facto standard for storing large datasets in data management systems such as Hadoop MapReduce, Hive, and Stratosphere. Though HDFS was originally designed to support scan-oriented operations, recently several techniques for HDFS have been developed to allow for efficient indexing. One of these indexing techniques is aggressive indexing, i.e. HDFS... View full abstract»

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  • Managing information for personal goals (vision)

    Publication Year: 2015, Page(s):30 - 33
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (161 KB) | HTML iconHTML

    This vision paper considers the challenge of providing users with control over how information about them is used, in an ecosystem of assistants that help people achieve personal goals based on lots of data about their activities. View full abstract»

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  • Large-scale spatial join query processing in Cloud

    Publication Year: 2015, Page(s):34 - 41
    Cited by:  Papers (9)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (161 KB) | HTML iconHTML

    The rapidly increasing amount of location data available in many applications has made it desirable to process their large-scale spatial queries in Cloud for performance and scalability. We report our designs and implementations of two prototype systems that are ready for Cloud deployments: SpatialSpark based on Apache Spark and ISP-MC based on Cloudera Impala. Both systems support indexed spatial... View full abstract»

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  • The era of big spatial data

    Publication Year: 2015, Page(s):42 - 49
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1280 KB) | HTML iconHTML

    The recent explosion in the amount of spatial data calls for specialized systems to handle big spatial data. In this paper, we discuss the main features and components that needs to be supported in a system to handle big spatial data efficiently. We review the recent work in the area of big spatial data according to these four components, namely, language, indexing, query processing, and visualiza... View full abstract»

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  • Data Engineering meets the Semantic Web (DESWeb)

    Publication Year: 2015, Page(s):50 - 52
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  • Graph mining meets the Semantic Web

    Publication Year: 2015, Page(s):53 - 58
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (478 KB) | HTML iconHTML

    The Resource Description Framework (RDF) and SPARQL Protocol and RDF Query Language (SPARQL) were introduced about a decade ago to enable flexible schema-free data interchange on the Semantic Web. Today, data scientists use the framework as a scalable graph representation for integrating, querying, exploring and analyzing data sets hosted at different sources. With increasing adoption, the need fo... View full abstract»

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  • Challenges in Chinese knowledge graph construction

    Publication Year: 2015, Page(s):59 - 61
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (159 KB) | HTML iconHTML

    The automatic construction of large-scale knowledge graphs has received much attention from both academia and industry in the past few years. Notable knowledge graph systems include Google Knowledge Graph, DBPedia, YAGO, NELL, Probase and many others. Knowledge graph organizes the information in a structured way by explicitly describing the relations among entities. Since entity identification and... View full abstract»

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  • SemTree: An index for supporting semantic retrieval of documents

    Publication Year: 2015, Page(s):62 - 67
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (687 KB) | HTML iconHTML

    In this paper, we propose SemTree, a novel semantic index for supporting retrieval of information from huge amount of document collections, assuming that semantics of a document can be effectively expressed by a set of 〈subject, predicate, object〉 statements as in the RDF model. A distributed version of KD-Tree has been then adopted for providing a scalable solution to the document i... View full abstract»

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  • Towards web-scale how-provenance

    Publication Year: 2015, Page(s):68 - 70
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (145 KB) | HTML iconHTML

    The annotation of data with meta-data, and its propagation through data-intensive computation in a way that follows the transformations that the data undergoes (“how-provenance”), has many applications, including explanation of the computation results, assessing their trustworthiness and proving their correctness, evaluation in presence of incomplete or probabilistic information, vie... View full abstract»

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  • Efficient OLAP operations for RDF analytics

    Publication Year: 2015, Page(s):71 - 76
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (322 KB)

    RDF is the leading data model for the Semantic Web, and dedicated query languages such as SPARQL 1.1, featuring in particular aggregation, allow extracting information from RDF graphs. A framework for analytical processing of RDF data was introduced in [1], where analytical schemas and analytical queries (cubes) are fully re-designed for heterogeneous, semantic-rich RDF graphs. In this novel analy... View full abstract»

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  • Big RDF data cleaning

    Publication Year: 2015, Page(s):77 - 79
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (185 KB) | HTML iconHTML

    Without a shadow of a doubt, data cleaning has played an important part in the history of data management and data analytics. Possessing high quality data has been proven to be crucial for businesses to do data driven decision making, especially within the information age and the era of big data. Resource Description Framework (RDF) is a standard model for data interchange on the semantic web. How... View full abstract»

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