Blockchain based Data Management, Analytics and Applications
- Mukesh Mohania, Ph.D
- IBM Distinguished Engineer and Master Inventor Member
- IBM Academy of Technology
Blockchain technology has gained massive attention in the recent years with significant expectations that it could transform every industry for its promise of providing a single “source of truths” for participants in a business network. Nevertheless, according to Garner's 2016 hype cycle for emerging technologies, blockchain is entering its peak of inflated expectations and in fact, its years to mainstream to adoption is still 5 to 10 years ahead before the technology reaches a plateau of productivity.
Since most of the blockchain efforts nowadays are still in a nascent state, we believe the time is right for database researchers and practitioners to get more deeply involved. In this talk, we put forward four components that researchers interesting in blockchain data management and analytics could increase focus on: (1) leverage query and transaction processing capabilities of mature data stores, (2) integrate on and off-chain data, (3) enable analytics services on blockchain as well as off-chain data, and (4) protect information from leakage. Finally, we will discuss number of blockchain applications in banking, insurance, and supply-chain areas.
Mukesh Mohania is an IBM Distinguished Engineer in IBM Research - Australia, and currently working in the areas of Blockchain, and Cognitive Data and Analytics. He has worked extensively in the areas of Information Management and Autonomic Computing. His work in these areas has led to the development of new products and also influenced several existing IBM products. He has received several awards within IBM, such as "Best of IBM", "Excellence in People Management", “Outstanding Innovation Award”, "Technical Accomplishment Award", “Leadership By Doing”, and many more. He has published more than 120 Research papers in International Conferences and Journals and also filed more than 80 patents in these or related areas, and more than 50 have already been granted. He is an IBM Master Inventor and a member of IBM Academy of Technology. He has held several visible positions in professional activities, like VLDB 2016 Conference Organizing Chair, ACM India VIce-President. He is an ACM Distinguished Scientist and currently chairing ACM Distinguished Service Award Committee in 2017-2018.
1. Encrypting and Querying OLAP Data Efficiently
- Alfredo Cuzzocrea, Ph.D
- Associate Professor
- Computer Science Engineering
- University of Trieste
Due to emerging technologies like Clouds, recently the problem of encrypting and querying big data is of great interest. Here, the main problem consists in devising effective and efficient encryption schemes for OLAP data, and then effective and efficient query algorithms for querying such data in their encrypted form directly. OLAP data are a knowledge-rich class of data that are extremely important for latest big data analytics tools. Inspired by this authoritative research trend, in this paper we provide the following contributions: (i) an overview of most relevant initiatives in the field; (ii) a reference architecture for querying encrypted OLAP data; (iii) discussion on open issues and research challenges that will dominate the future scene of the investigated research topic.
Alfredo Cuzzocrea is currently Associate Professor in Computer Science Engineering at the DIA Department, University of Trieste, Italy. He is also habilitated as Full Professor in Computer Science Engineering by the French National Scientific Habilitation of the National Council of Universities. He holds several Visiting Professor positions worldwide (Europe, USA, Asia, Australia) and roles in international scientific societies, steering committees for international conferences, and international panels. He also actively contributes the research community by covering a large collection of roles in top-quality conferences and journals. His research interests mostly focus on big data management, processing, and analytics.
2. Data and Visual Analytics for Emerging Databases
- Carson Leung, Ph.D
- Senior member of ACM and IEEE
- University of Manitoba
With advances in technology, high volumes of valuable data of different veracity can be generated at a high velocity in wide varieties of data sources in various real-life applications. As a popular data mining tasks, frequent pattern mining discovers implicit, previously unknown and potentially useful knowledge in the form of sets of frequently co-occurring items or events. Many existing data mining algorithms return to users with long textual lists of frequent patterns, which may not be easily comprehensible. Given a picture is worth a thousand words, having a visual means for humans to interact with computers would be beneficial. This talk presents a system for data and visual analytics for emerging databases.
Carson Leung is currently a Full Professor at the University of Manitoba, Canada. He obtained his BSc(Hons), MSc and PhD from the University of British Columbia, Canada. He has published more than 170 papers on the topics of databases, data mining, big data computing, social network analysis, as well as visual analytics--including papers in ACM Transactions on Database Systems (TODS), Social Network Analysis and Mining (SNAM), Future Generation Computer Systems (FGCS), Journal of Organizational Computing and Electronic Commerce (JOCEC), IEEE International Conference on Data Engineering (ICDE), IEEE International Conference on Data Mining (ICDM), and Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD). Over the past few years, he has served as (i) a General Chair of IEEE CBDCom 2016, (ii) a Program Chair of IEEE HPCC 2016 and BigDAS 2017, (iii) an organizing committee member of ACM SIGMOD 2008, IEEE ICDM 2011, IEEE/ACM ASONAM 2014, and IEEE BigComp 2017, as well as (iv) a PC member of numerous international conferences including ACM KDD, ACM CIKM, and ECML/PKDD. He is a senior member of the ACM and of the IEEE.
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