The following workshops are all co-located with the EDBT/ICDT 2015 conferences in Brussels, Belgium. The workshops are all held on Friday, March 27, 2015. More detailed descriptions and web links will be provided soon.
The importance of “big data" and systems for supporting queries and analytics over big data should require no explanation. The purpose of this workshop is to explore algorithms and models of computation using the specialized programming systems and data-management systems that have been developed over the past few years.
Organizers: Witold Andrzejewski (Poznan University of Technology), Sebastian Breß (TU Dortmund University), Max Heimel (Technische Universität Berlin)
The goal of this workshop is to investigate challenges and opportunities for data processing on existing and upcoming heterogeneous hardware architectures. Increased heterogeneity is one of the major current challenges in data processing on modern hardware. With multi-core CPUs, graphics cards, massively parallel accelerator cards (e.g. Intel Xeon Phi), heterogeneous mobile processors (e.g. ARM big.LITTLE) and FPGAs, we already face a huge variety of available processing devices with different capabilities, strengths and weaknesses. This trend is expected to accelerate in the near future, and tomorrow’s database systems will need to exploit and embrace this increased heterogeneity in order to keep up with the performance requirements of the modern information society.
Organizers: Torben Bach Pedersen, (Aalborg University), Wolfgang Lehner (TU Dresden)
This workshop focuses on conceptual and system architecture issues related to the management of very large-scale data sets specifically in the context of the energy domain. The overall goal is to bridge the gap between domain experts and data management scientists on the one hand. On the other hand, the workshop’s goal is to create awareness of this upcoming and very challenging application area. For the workshop’s research program, we are seeking contributions that push the envelope towards novel schemes for large-scale data processing with special focus on energy data management.
Organizers: Alexander Artikis (NCSR Demokritos), Antonios Deligiannakis (Technical University of Crete)
The Big Data era has posed a number of challenges in applications related to event processing. In particular, the data volume, velocity and distribution necessitate the design on new scalable approaches for the efficient and timely processing of the produced data. The lack of veracity in the handled data/events further complicates the problem. Moreover, key challenges concern the use of the voluminous data in order to forecast future events and perform proactive event-driven decision- making.
Event forecasting is important because eliminating or mitigating an anticipated problem, or capitalizing on a forecast opportunity, can substantially improve our quality of life, and prevent environmental and economic damage. For example, changing traffic-light priority and speed limits to avoid traffic congestions will reduce carbon emissions, optimize transportation and increase the productivity of commuters. At the business level, making smart decisions ahead of time can become a differentiator leading to significant competitive advantage. In a wide range of applications, prevention is more effective than the cure. To prevent problems and to capitalize on opportunities before they even occur, a proactive event-driven decision-making paradigm is necessary. Decisions are triggered by forecasting events instead of reacting to them once they happen. Moreover, decisions are made in real-time and require on-the-fly processing of Big Data, that is, extremely large amounts of noisy data flooding in from various locations, as well as historical data. The aim of the EPForDM workshop is to bring together computer scientists with interests in the fields of event processing, event forecasting and event-driven decision-making to present recent innovations, find topics of common interest and stimulate further development of new approaches to make sense of Big Data.
Querying Graph Structured Data (GraphQ)
Organizers: Federica Mandreoli (University of Modena and Reggio Emilia) , Riccardo Martoglia (University of Modena and Reggio Emilia), Wilma Penzo (University of Bologna)
The growing scale and importance of graph data in several database application areas has recently driven much research efforts towards the development of data models and technologies for graph- data management.
Life science databases, social networks, Semantic Web data, bibliographical networks, knowledge bases and ontologies, are prominent examples of application domains exhibiting data that is natural to represent in graph-based form. Datasets in these domains are often characterized by heterogeneity, complexity and largeness of contents that make the querying experience a really challenging task.
The overall goal of the GraphQ workshop is to bring people from different fields together, exchange research ideas and results, and encourage discussion about how to efficiently and effectively support graph queries in different application domains. GraphQ seeks at providing the opportunity for inspiration and cross-fertilization for the many research groups working on graph-structured data, with a particular focus on the querying issues.
The workshop will welcome innovative papers from academic and industrial researchers in the fields of information retrieval, relational databases, Semantic Web, streaming data management, pattern matching, biological databases, social networks, human-computer interaction, and other related areas.
Organizers: Devis Bianchini (Università di Brescia), Valeria De Antonellis (Università di Brescia), Roberto De Virgilio (Università Roma Tre)
The joint application of data management and Semantic Web competencies, through the design of new models, languages and tools, has turned out to be very useful to enable the use of the Web as a huge, interlinked, dynamic repository of linked resources. The contributions and discussions born and developed during four previous editions of the “Linked Web Data Management” (LWDM) workshop allowed to meet our goal of introducing a data management perspective within the Linked Data world, previously focused on publishing, retrieving, querying, browsing and mashing- up the ever growing amount of linked data in a meaningful way. The maturity gained by the workshop also enabled to introduce in every edition new issues related with the main topics. The fourth edition has been characterized by fruitful contributions on the combination of knowledge coming from data management, Semantic Web and Linked Data fields in order to better exploit the Web 2.0 potential, where people and applications discover new linked information in an un- expected way, according to an explorative perspective, moving through linked sources of knowledge. This enabled the study of Linked Data issues within a social perspective of the Web, where also the relationships between users might play a crucial role in finding the right resources in an efficient way. Recently, the scientific community raised the need of facing the quantity and the heterogeneity of data made available on the Web, also managing with the rapidity which such data are distributed with (Big Data issues). These problems also feature the Linked Data world to access and explore linked resources, thus requiring innovative application of data management tools. Research efforts of the Semantic Web community are being devoted to investigate the relationships between Big Data, Linked Data and data-intensive applications
Organizers: Traian Marius Truta, (Northern Kentucky University), Li Xiong, Emory University), Farshad Fotouhi, (Wayne State University)
Organizations collect vast amounts of information on individuals, and at the same time they have access to ever-increasing levels of computational power. Although this conjunction of information and power provides great benefits to society, it also threatens individual privacy. As a result legislators for many countries try to regulate the use and the disclosure of confidential information. Various privacy regulations (such as USA Health Insurance Portability and Accountability Act, Canadian Standard Association’s Model Code for the Protection of Personal Information, Australian Privacy Amendment Act, etc.) have been enacted in many countries all over the world. Data privacy and protecting individuals’ anonymity have become a mainstream avenue for research. While privacy is a topic discussed everywhere, data anonymity recently established itself as an emerging area of computer science. Its goal is to produce useful computational solutions for releasing data, while providing scientific guarantees that the identities and other sensitive information of the individuals who are the subjects of the data are protected.
The proposed workshop aims to provide an open yet focused platform for researchers and practitioners from computer science and other fields that are interacting with computer science in the privacy area such as statistics, healthcare informatics, and law to discuss and present current research challenges and advances in data privacy and anonymity research. We welcome original research papers that present novel research ideas, position papers that discuss new technology trends and provide new insights into this area, integrative papers that present interdisciplinary research in the privacy area, as well as industry papers that share practical experiences.