PADG 2017 - 1st IEEE Big Data International Workshop on Policy-based Autonomic Data Governance

Venue: PADG 2017 will be part of 15th IEEE International Conference on Big Data (Big Data 2017), December 11-14, 2017, Boston, MA, USA.

 

The proliferation of IoT devices has led to the production of large volumes of data that can be used to characterize and potentially optimize real world processes. At the same time, the influence of edge computing is leading to more distributed architectures incorporating more autonomous elements. The flow of information is critical in such environments, but the real time, distributed nature of the system components complicates the mechanisms for managing operations, and protecting and controlling access to data. A promising direction for the management of complex distributed environments is to make the major elements of the system self-describing and self-managing. This would lead to an architecture where policy mechanisms are tightly coupled with the system elements. In such integrated architectures, we need to create new models for information assurance. For example, policies can capture information on how data must be processed in different components of the system, providing a new model for traceability of information, and allowing better provenance on information flows. Policies can also control data collection, transfer and use across different components of the systems. It is also critical that data governance mechanisms be scalable and reliable.

In this workshop, we aim to focus primarily on policy based mechanisms for data governance in distributed systems. PADG 2017 will consider original and unpublished research articles that propose bold steps towards addressing the challenges of data management and security in multi-site, interconnected processing environments.

 

Topics of interest to the workshop include but are not limited to:

  • Policy Mechanisms for Collaborative Systems
  • Management of Data and Information Quality
  • Architectures for Autonomic Computing and Cyber-infrastructures
  • Management of Data Security, Privacy and Trust
  • Modelling and Analysis of Policy Interactions
  • Impact Analysis Strategies and Algorithms
     

Important Deadlines:

October 10, 2017: Submission Deadline

November 1, 2017: Authors Notification

November 15, 2017: Camera-ready & Registration

December 11-14, 2017: Workshops

Paper Submission Instructions:

Paper Submission URL: https://wi-lab.com/cyberchair/2017/bigdata17/scripts/ws_submit.php

The requirements for the Workshops are the same as those for the conference - "In the  conference/workshop proceedings, the regular paper is 10 pages, short paper is 6 pages and extended abstract paper is up to 3 pages. Papers should be formatted to IEEE Computer Society Proceedings Manuscript Formatting Guidelines."
Please see http://cci.drexel.edu/bigdata/bigdata2017/CallPapers.html (section on Paper Submission) for templates.

Keynote Address:

Title: My Fair (Big) Data
Abstract: 
Policy making has the strict requirement to rely on quantitative and high quality information. This requirement becomes even stricter when policy making is not done by humans but the policy can emerge automatically from collaborative environments. In addition, exploiting the potential information that Big Data can provide is a great opportunity for the policy making task. In this context, the quality of available data plays a fundamental role together with the ability of extracting meaningful information from the heap. This talk will address the data quality issue by showing how to deal with Big Data quality in the different steps of a processing pipeline. This pipeline involves several phases, the most relevant being: an acquisition phase, where the trustfulness of the Big Data source needs to be evaluated; a cleaning phase, which can be very complex and can involve a plethora of methods, including inconsistency detection and correction and outlier management; an interpretation phase, where the results of the analyses and predictions need to be carefully evaluated. Among the important aspects to be considered, especially in the interpretation phase, are: (i) representativeness and (ii) accuracy of predictions. In terms of representativeness, given the uncontrolled nature of the mechanisms that generate Big Data, sources can be selective and not representative. Conversely, in traditional official statistical analyses, for instance, surveys are “designed” in a controlled way, so that the collection and the subsequent analysis phases can rely on the features of such a design (e.g. sampling characteristics). The accuracy of predictions based on Big Data is anotherrelevant aspect. The case of Google FluTrends is emblematic of several challenges related to the Big Data “hubris”: (i) evaluation of robustness over time of models based on Big Data that may exhibit unexpected glitches; (ii) evaluation of the usage of Big data-based models alone or in conjunction with more traditional sources; for instance, an accurate interpretation of the analyses performed on Big Data sources may reveal that they are not able to replace traditional sources but rather to have a complementary role. The integration phase is a further relevant phase of the Big Data processing pipeline: Big data sources can be integrated with more traditional ones, like, e.g., structured data collected through surveys. A relevant role in this phase is played by metadata that permit to clearly represent the meaning of the specific data at hand. For the purpose of integrating Big data sources and traditional sources, ontologies could be a key solution. Integration systems relying on ontologies could also permit a formal quality evaluation of inaccuracy, inconsistency and incompleteness of integrated data.

Speaker Bio

Tiziana Catarci is full professor in Computer Science and Engineering at Sapienza University of Roma and director of the ECONA Interuniversity Research Centre on the Cognitive Processing in Natural and Artificial Systems and of the Sapienza Design Research center. During 2010-14 she has been Sapienza vice-rector for technologies and infrastructures and during 2009-2014 she has been president of the Sapienza ICT service center. Tiziana Catarci’s main research interests are in theoretical and application oriented aspects of visual information access, human-computer interaction, user interfaces, usability, smart environments, information visualization, data management, data quality and digital libraries.  On these topics she has published over 200 papers in international journals, conferences and workshop proceedings, and 20 books. Her contribution can be regarded as one of the first and most significant examples of deep analysis and formalization of the interaction between the user and the database, which takes in consideration both usability issues and language related aspects. Dr. Catarci is the Editor-in-Chief of the ACM Journal of Data and Information Quality. She is regularly in the programming committees of the main database and human-computer interaction conferences and is associate editor of the World Wide Web Journal and the Journal of Data Semantics. She has been co-chair of several important international conferences in both database and hci areas. In particular, in 2008, she has been the Co-chair of the 2008 edition of the largest and most important conference on human-computer interaction, ACM CHI. In 2003 she has been the recipient of the IBM Eclipse Innovation Award. In 2016 she has been nominated member of the prestigious European Academy of Sciences and Arts. In 2016 she has been included among the “100 Women for Science” project - http://www.100esperte.it/. In 2017 she received the Levi Montalcini Association award for the "diffusion of scientific culture among younger generations".

Program Co-Chairs:

  • Seraphin B. Calo, IBM Research, Yorktown Heights, NY, USA
  • Elisa Bertino, Purdue University West Lafayette, IN, USA
  • Dinesh C. Verma, IBM Research, Yorktown Heights, NY, USA

Program Committee Members:

  • Saritha Arunkumar, IBM Hursley Labs, UK
  • Raouf Boutaba, University of Waterloo, Waterloo, Ontario, Canada
  • Tereza Carvalho, University of Sao Paulo, Brazil
  • Tiziana Catarci, University of Rome, Italy
  • Supriyo Chakraborty, IBM Research, Yorktown Heights, NY, USA
  • Eduard Dragut, Temple University, Philadelphia, PA, USA
  • Jorge Lobo, University Pompeu Fabra, Barcelona, Spain
  • Emil Lupu, Imperial College London, London SW7 2RH, UK
  • Brian Rivera, Army Research Labs, Adelphi, MD, USA
  • Munindar Singh, North Carolina State University, Raleigh, NC, USA
  • Anna Squicciarini, Penn State University, University Park, PA, USA
  • Maroun Touma, IBM Research, Yorktown Heights, NY, USA
  • Christopher Williams, UK Dstl, Porton Down, Wiltshire SP4 0JQ, UK