The 18th International Conference on Discovery Science (DS 2015) will be held in Banff (Canada), on 4-6 October 2015, and provides an open forum for intensive discussions and exchange of new ideas among researchers working in the area of Discovery Science. The scope of the conference includes the development and analysis of methods for discovering scientific knowledge, coming from machine learning, data mining, and intelligent data analysis, as well as their application in various scientific domains. We welcome papers that focus on the analysis of different types of complex data, such as structured, spatio-temporal and network data. We particularly welcome papers addressing applications. Finally, we would like to encourage contributions from the areas of computational scientific discovery, mining scientific data, computational creativity and discovery informatics.
For more information: https://ds2015.cs.dal.ca/
The Institute for Big Data Analytics was privleged to receive a visit on October 17 from Mr. Ed Holder, Minister of State for Science and Technology. Mr Holder had a tour of the Institute, spent time talking to students about their research and listened to a presentation by Dr. Stan Matwin. Mr. Holder seemed impressed by the diversity of research projects and affirmed the importance of this field to innovation and economic prosperity. Although he had come to Halifax for a different meeting, the visit to the Institute was the one diversion in his trip that he insisted on making.
Other departments of the Government of Canada have also been making contact with the Institute for Big Data Analytics. Recent meetings have been held with Deputy Ministers and senior bureaucrats in the Canada Revenue Agency, the Privy Council Office, and Employment and Social Development Canada.
The paper, "Linked Open Data Driven Game Generation" has been accepted to the in-use track of The 13th International Semantic Web Conference (ISWC2014) [1]. The authors are Rob Warren (Dalhousie Institute for Big Data Analytics, and Erik Champion School of Media, Culture and Creative Arts, Curtin University, Australia.
"Linked Open Data provides a means of unified access to large and complex interconnected data sets that concern themselves with a surprising breath and depth of topics. This unified access in turn allows for the consumption of this data for modelling cultural heritage sites, historical events or creating serious games. In the paper we present our work on simulating part of a Great War battle using data from multiple Linked Open Data projects in a mechanized fashion. In this case study we report on the experience of building a prototype simulation engine using Linked Open Data and provide some observations on balancing realism and historical accuracy against available data."
On Thursday July 10 the Globe and Mail published an article on Big Data by Bryan Borzykowski entitled "Do Companies Pin Too Much Faith in Big Data?". The article features input from Stan Matwin and from Pat Finerty, vice-president, alliances and business development for SAS Canada. Visit the Globe and Mail to read the full article.
Thomas Trappenberg participated in the OBI's Brain-CODE Analytics Workshop on May 28/29, 2014 on behalf of the Dalhousie Institute for Big Data analytics. OBI is the Ontario Brain Institute, a major initiative by the province of Ontaria to develop brain science into practical medical solutions. This is an urgent topic given the expected but dramatic increase of neurological disorders in our aging population. While this non-profit research institute is mainly sponsored by the province of Ontario, there are an increasing number of partners in other provinces and internationally.
The focus of the workshop was on the analytics of brain data. Topics included presenting and communicating data, fostering specific brain initiatives, software infrastructures to share and mine brain data, and issues around security and privacy concerns for health data. An increased need for education in this area was also mentioned.
The participants were very enthusiastic about the opportunities in finding health solutions with the help of increasingly available brain data. Several non-profit organizations and international companies were also present. Thomas gave an outline of machine learning techniques to health data that was well received. It would be very desirable if our province could partner with OBI by sharing some of our data and to contribute with our expertise on big data analytics.
Title: Lifelong Machine Learning and Reasoning
Speaker: Danny Silver, Acadia
Date: Wednesday June 11, 2014
Time: 11:30am
Location: Slonim Room (430) - Faculty of Computer Science, Dalhousie University, Halifax.
Abstract:
Lifelong Machine Learning (LML) considers intelligent systems that learn many tasks over a lifetime, accurately and efficiently retaining the knowledge they have learned and using that knowledge to more quickly and accurately learn new tasks. In this tutorial we will review a framework for LML, define its requirements, and present solutions for the key problems that involve knowledge consolidation and transfer learning using multiple task learning methods. Links to artificial general intelligence and neural-symbolic integration are made. The final part of the talk will discuss recent work on extending LML to the learning of common background knowledge for the purposes of reasoning (this extension we call Lifelong Machine Learning and Reasoning, or LMLR). Opportunities for advances in artificial intelligence lie at the locus of machine learning and knowledge representation; specifically, knowledge consolidation can provide insights into common knowledge representation for use in learning and reasoning. This talk is for those who feel it is time for the machine learning community to move beyond learning algorithms to systems that are capable of learning, retaining and using knowledge over a lifetime.
Speaker Bio:
Dr. Danny Silver is the Director of the Acadia Institute for Data Analytics. He is also a professor in and the former Director of the Jodrey School of Computer Science at Acadia University. His areas of research and development are machine learning, data mining, and adaptive systems. He has published over 60 scientific papers, edited special journal editions, and has co-chaired or been part of the program committee for a number of national and international conferences, seminars and workshops. He is on the editorial board for the Journals of Artifical General Intelligence and Brain Informatics and was the President of the Canadian Artificial Intelligence Association (CAIAC) from 2007-2009. Danny has held a NSERC Discovery Grant since 2000, and most recently was awarded a Harrison McCain Foundation Award for research into advance machine learning methods. Since 1993, he has worked on machine learning and data mining projects in the private and public sector providing situation analysis and problem definition, project management and guidance, and predictive analytic services. In 2011, he received the Science Champion Award from the Nova Scotia Discovery Center for his work on youth robotics and the advancement of STEM education.
Title: Diversity and its Applications in Search, Summarisation and Related Information Selection Tasks
Speaker: Marcin Sydow, Polish Academy of Sciences and Polish-Japanese Institute of IT
Date: Thursday June 12, 2014
Time: 11:30am
Location: Slonim Room (430) - Faculty of Computer Science, Dalhousie University, Halifax.
Abstract:
The concept of diversity has recently gained increasing interest in a wide range of applications including: web search, database querying, recommender systems, and automatic summarisation. The approach consists in returning to the user the set of information items (e.g. search results, or recommended items, etc.) that is not only generally relevant but also diversified. The rationale behind diversfying the result set is to reduce potential result redundance. This allows covering of potentially many various aspects or views, to at least partially satisfy the unkown user's information needs, expressed in a potentially ambiguous query. We will present various approaches, based on combinatorial optimisation (e.g. on Facility Dispersion Problem), as well as on probabilitic techniques. We will also briefly present some recent results and examples of applications including: diversified entity summarisation on semantic knowledge graphs (DIVERSUM), diversified search engine query suggestions or diversity-aware query-by-example entity search (QBEES). We will also discuss some ongoing work and future directions.
Speaker Bio:
Dr. Marcin Sydow is the Head of Web Mining Lab and of the Chair of Intelligent Systems, Algorithms and Mathematics at Polish-Japanese Institute of IT as well as Assistant Professor at Polish Academy of Sciences, Institute of Computer Science, Warsaw Poland. He received M.Sc. (Mathematics) from Warsaw University and Ph.D (Computer Science) from Polish Academy of Sciences. His research interests include Web Search, Web Mining, algorithms, elements of AI and Natural Language Processing. He has published over 50 scientific publications and serves as a PC member and reviewer in many international conferences and journals. He received several prizes (ECML/PKDD 2007 Discovery Challenge, 2008 DAAD Prize for funding research visit at Max-Planck Institute, Saarbruecken, etc.) and several state grants for conducting research in Poland in 2011-2015. His recent research interests include the concept of diversity in information sciences and novel information-processing algorithms for semantic knowledge graphs.
The White House issued a new report, on May 1, 2014 on how big data will transform the way we live and work and alter the relationships between government, citizens, businesses, and consumers. The report focuses on how the public and private sectors can maximize the benefits of big data while minimizing its risks. It also identifies opportunities for big data to grow the economy, improve health and education, and make the nation safer and more energy efficient. To read the full report download it from the White House website:
http://www.whitehouse.gov/sites/default/files/docs/big_data_privacy_report_5.1.14_final_print.pdf
Stan Matwin was a keynote speaker at the 2014 Desigining Productivity Conference organized by the Department of Industrial Engineering at Dalhousie. Amongst other activities, the conference discussed the OneNS/Ivany Report with commission member and Nova Scotia business leader Irene d’Entremont and other notable business leaders and strategic thinkers including, Bill Black and JP Deveau (President of Acadian Seaplants ltd.). The agenda also included speakers from the Barrington Consulting Group Inc., Scotsburn Dairy Group.
And, in May 29-30 Stan participated in the ERCIM Expert Group on Big Data Analytics in Tirenia, Italy. The objective of this Expert Group is to gather experts on the many faces of Big Data Analytics and compile with their help a white paper that fosters a future agenda for European research on this direction.
Sensing big data at a societal scale, and the transparent interlinking of digital and physical reality, has the potential of providing a powerful social microscope, which can help us understand many complex and hidden socio-economic phenomena, such as mobility behaviors, economic and financial crises, the spread of epidemics, the diffusion of opinions and so on. It is clear that such challenge requires high-level analytics, modeling and reasoning across all the social dimensions.
In practice, however, there is a big gap from the opportunities offered by the big data to the challenges posed by social, economical, scientific phenomena: Big data are fragmented and low-level. They reside in diverse databases and repositories, often inaccessible for proprietary and legal constraints, and have limited power to portray different social dimensions. There are many regulatory, business and technological barriers to set the power of big data free.
As a response, the paradigm of Big Data Analytics is emerging at the convergence of several disciplines, including machine learning, data mining, statistics, complex systems, socioeconomic sciences, etc. There is a flourishing body of research about making sense of Big Data, but a coherent scientific and technological framework is still missing. We need to put at work scientists and technologists from different disciplines to shape a research and innovation agenda that might drive the ERICM future actions.
The ERCIM group involves prominent research leaders in industry and academia to share their visions and elaborate together what should be the challenging research issues in this promising research frontier.