32nd IEEE International Conference on Data Engineering

May 16-20, 2016 · Helsinki, Finland

It is with great pleasure that the organizers of the 32nd IEEE International Conference on Data Engineering invite you to take part in ICDE 2016 to be held in Helsinki, Finland, from May 16 to 20, 2016. As the capital of Finland, Helsinki is a vibrant city on the Baltic Sea renowned for its design and friendly atmosphere. The venue is at Aalto University's School of Business, which is located right in the city center.
  • Helsinki_City
  • Finlandia_Hall
  • Venue
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Workshops Hosted by ICDE 2016

All workshops are held at the same venue as the ICDE 2016 conference, in Helsinki, at the Aalto University School of Business.

CloudDM - Workshop on Cloud Data Management

  • Workshop Organizers:
    • Barzan Mozafari (University of Michigan, USA)
    • Ippokratis Pandis (Amazon Web Services, USA)
  • Date: Monday May 16, 2016.
  • Overview: Cloud computing has emerged as a promising computing and business model. By providing on-demand scaling capabilities without any large upfront investment or long-term commitment, it is attracting a wide range of users. The database community has also shown great interest in exploiting this new platform for data management services in a highly scalable and cost-efficient manner. Cloud computing presents challenges and opportunities for data management.

    The Cloud Data Management (CloudDM) workshop aims to bring researchers and practitioners in cloud computing and data management systems together to discuss the research issues at the intersection of those areas, share experiences in building and managing cloud-scale data management services, and also to draw more attention from the larger data management research community to this highly promising field.
  • Website: CLOUDM 2016

HDMM 2016 - Health Data Management and Mining

  • Workshop Organizers:
    • Shourya Roy (Xerox Research Centre India, India)
    • Anupam Joshi (University of Maryland, USA)
    • Sandya Mannarswamy (Xerox Research Centre India, India)
    • Praveen Rao (University of Missouri-Kansas City, USA)
  • Date: Monday May 16, 2016.
  • Overview: Health data originate from a wide variety of heterogeneous data sources which include medical literature, Electronic Medical Records (EMRs), medical imaging data, time series data from ICU/in-hospital sensors, insurance claims data, wearable sensors, mobile health applications data, omics data etc. just to name a few. However, unlike many other domains much of these information remain in paper form, lack common standards, are not shared and frequently hampered by the lack of fool-proof de-identification for patient privacy. Much of healthcare data remains hidden as unstructured data in the form of clinical notes, imaging reports, patient narratives etc.

    Effective integration and management of these multiple heterogeneous data sources and mining them for actionable insights requires inter-disciplinary research across multiple domains of computer and medical sciences. The goal of this workshop is to bring together researchers cross-cutting the fields of data management and medical informatics to discuss the unique challenges in health care data management and to propose novel and practical solutions for next generation 'data driven' healthcare systems. The workshop is intended to facilitate cross-disciplinary research collaboration to develop innovative solutions for healthcare data management and mining, effectively breaking the 'data silos' barrier across the diverse health data sources. We also intend to discuss potential of creating common datasets for future research which can be made available publicly by addressing concerns such as privacy.

    The topics of interest include but are not limited to:
    • Big data integration of heterogeneous health data sources.
    • Web-scale & cloud based medical data management systems.
    • NoSQL/NewSQL/Graph databases for healthcare data management.
    • Novel visualization of healthcare data in complex and critical medical environments.
    • Natural language processing and text mining techniques for health data mining.
    • Semantic Web techniques for multi-sourced healthcare data.
    • Knowledge and non-obvious relation extraction across multiple medical data sources.
    • Social and personalized health data mining.
    • Handling noisy and missing medical data.
  • Website: HDMM 2016

DESWeb 2016 - 7th International Workshop on Data Engineering meets the Semantic Web

  • Workshop Organizers:
    • Theodore Dalamagas (ATHENA Research and Innovation Center, Greece)
    • Sabrina Kirrane (Vienna University of Economics and Business, Austria)
    • Yannis Stavrakas (ATHENA Research and Innovation Center, Greece)
  • Date: Monday May 16, 2016.
  • Overview: DESWeb 2016 workshop aims to bring together researchers in the intersection of Database and the Semantic Web communities, focusing on the application of such technologies in solving data engineering and data management problems in the Web of Data. The Database community has increasingly become interested in the Semantic Web. Given the recent explosion of structured data available on the Web, and the associated challenges with respect to what is commonly referred to as the 3 data V's (Volume, Variety, and Velocity), the two communities form excellent candidates to benefit from their mutual expertise and insights.

    Issues such as storage and management of large datasets and workloads, query processing, SPARQL query optimization, record linkage and semantic disambiguation form a fertile ground for scalable algorithms and methods of data management in the contexts of Big Data and the Web of Data. Furthermore, the application of existing and well-established Data Management techniques on the Semantic Web context can provide valuable insights and give growth to novel research contributions to tackle existing problems.

    DESWeb provides a forum that will bring together the Database and Semantic Web communities, highlighting their common interests and promoting mutual advancement and cross-fertilization.
  • Website: DESWeb 2016

HardBD 2016 - Big Data Management on Emerging Hardware

  • Workshop Organizers:
    • Shimin Chen (Chinese Academy of Sciences, China)
    • Binsheng He (Nanyang Technological University, Singapore)
    • Xiaofeng M. Xiaofeng (Renmin University of China, China)
  • Date: Friday May 20, 2016.
  • Overview: Data properties and hardware characteristics are two key aspects for efficient data management. A clear trend in the first aspect, data properties, is the increasing demand to manage and process Big Data in both enterprise and consumer applications, characterized by the fast evolution of 'Big Data Systems'. Examples of big data systems include NoSQL storage systems, MapReduce/Hadoop, data analytics platforms, search and indexing platforms, messaging infrastructures, event log processing systems, as well as novel extensions to relational database systems. These systems address needs for processing structured, semi-structured, and unstructured data across a wide spectrum of domains such as web, social networks, enterprise, mobile computing, sensor networks, multimedia/streaming, cyber-physical and high performance systems, and for a great many application areas such as e-commerce, finance, healthcare, transportation, telecommunication, and scientific computing. At the same time, the second aspect, hardware characteristics, is undergoing rapid changes, imposing new challenges for the efficient utilization of hardware resources. Recent trends include massive multi-core processing systems, high performance co-processors, very large main memory systems, storage-class memory, fast networking interconnects, big computing clusters, and large data centers that consume massive amounts of energy.

    Utilizing new hardware technologies for efficient Big Data management is of urgent importance. However, many essential issues in this area have yet to be explored, including system architecture, data storage, indexes, query processing, energy efficiency and proportionality, and so on. The aim of this half-day workshop is to bring together researchers, practitioners, system administrators, and others interested in this area to share their perspectives on the efficient management of big data over new hardware platforms, and to discuss and identify future directions and challenges in this area.
  • Website: HardBD 2016

KEYS 2016 - The Fourth International Workshop on Keyword Search and Data Exploration on Structured Data

  • Workshop Organizers:
    • Yi Chen (New Jersey's Science & Technology University, USA)
    • Jiaheng Lu (University of Helsinki, Finland)
  • Date: Friday May 20, 2016.
  • Overview: Information search has a close relationship with our daily lives, for example, the wide use of Web search engines to search for various data and information, across medias such as textual documents, images, and video. Vast amounts of data have been accumulated on the Web and in the data repositories of enterprises, in the form of relational databases, XML, JSON and structured data embedded in text documents. Accessing these data in a traditional way requires users to have a good knowledge of structured query languages, and an accurate understanding of data schemas. In contrast, data exploration, including keyword search, querying by example and visualization enable users to access heterogeneous representations of data in a natural and convenient manner, hence greatly improving the usability of the underlying data; this is one of the reasons why both database and information retrieval communities have a growing research interest in this area in recent years. In fact, keyword search and data exploration on structured data present both challenges (e.g., the queries are inherently ambiguous and complex) and opportunities (e.g., judicious use of the meta-information can enhance the search result quality).

    Apart from the structured and semi-structured data as represented in the form of relational databases and XML documents, we have witnessed a burgeoning interest in data exploration on new forms of data, for example, JSON collections, spatial and multimedia data. In addition, large amounts of social media data are being created every minute on the Internet, including blogs, user comments, and tweets. We can exploit the structures within and across the social media documents to improve search quality and detect many useful information and relations that are hard to find by conventional approaches using only the unstructured characteristics.

    The aim of this workshop is to provide a forum for both academic researchers and industrial practitioners to discuss opportunities and challenges in keyword search and data exploration for structured data, and to present the key issues and novel techniques in this area. In this workshop, we invite researchers from academia and industry working in relational databases, data warehouses, NoSQL databases, information extraction, natural language processing, probabilistic databases, social networks, social media data, spatial data, and related areas to submit their original papers.
  • Website: KEYS 2016