14.00, room 445, PCRI

Abstract
An integral part of many data-intensive applications is the need to collect and analyze enormous data sets, such as click streams, search logs, and sensor streams to derive answers and insights with low latencies. Concurrently, new programming models and architectures have been developed for large-scale cluster computing, exemplified by recent MapReduce systems. However, these systems are designed for batch processing and require data set to be fully loaded into the cluster before running analytical queries, hence causing high delays of query answers.

In this talk, I present the design of a scalable, low-latency analytics platform, called Scalla, that fundamentally transforms the existing cluster computing paradigm into an incremental parallel processing paradigm, which provides the combined benefits of massive parallelism, incremental answers, and I/O efficiency. Our technical contributions include replacing an existing popular mechanism for partitioned parallelism with a purely hash-based mechanism and using dynamic frequency analysis to offer in-memory processing for most of the data. In this talk, I will also examine two application scenarios, click stream analysis, which has been used in our evaluation, and genomic data analysis, which is a new project that leverages Scalla for massive-scale genomic data processing and analysis.

Short bio
Yanlei Diao is an Associate Professor of Computer Science at the University of Massachusetts Amherst. Her research interests are in information architectures and data management systems, with a focus on large-scale data analysis, data streams, uncertain data management, and flash memory databases. She received her PhD in Computer Science from the University of California, Berkeley in 2005, her M.S. in Computer Science from the Hong Kong University of Science and Technology in 2000, and her B.S. in Computer Science from Fudan University in 1998.

Yanlei Diao was a recipient of the NSF Career Award and the IBM Scalable Innovation Faculty Award, and was a finalist of the Microsoft Research New Faculty Fellowship. She spoke at the Distinguished Faculty Lecture Series at the University of Texas at Austin. Her PhD dissertation “Query Processing for Large-Scale XML Message Brokering” won the 2006 ACM-SIGMOD Dissertation Award Honorable Mention. She is an associate editor of PVLDB 2013 and has served on the organizing committees of SIGMOD, CIDR, DMSN, the New Researcher Symposium, and the New England Database Summit. She has served on program committees of numerous international conferences and workshops.

11.00, room 445, PCRI

Abstract
Highly heterogeneous data have boomed during the last decade, due to their largely distributed way of production: corporations of any size, individual users as well as automatic extraction tools have contributed a constantly increasing volume of heterogeneous and noisy information. Entity Resolution (ER) helps to reduce the corresponding entropy by identifying those pieces of information that refer to the same real-world objects.

Typically, blocking techniques are used to scale ER to large volumes of data. However, most of these techniques rely on schema information and are inapplicable to highly heterogeneous settings. Our work goes beyond existing blocking techniques, by introducing a novel methodology that is inherently crafted for voluminous, highly heterogeneous, and noisy data collections.

At the core of our approach lie three independent, but complementary steps: block-building (using redundant block assignments for effectiveness), meta-blocking (reducing the number of necessary blocks), and block processing (increasing efficiency of ER operations). Our experimental evaluation with three large-scale, real-world data sets demonstrates that our methodology can successfully handle very large and highly heterogeneous datasets, achieving an excellent balance between effectiveness and efficiency.

Short bio
Themis Palpanas is a professor of computer science at the University of Trento, Italy. He received the BS degree from the National Technical University of Athens, Greece, and the MSc and PhD degrees from the University of Toronto, Canada. Before joining the University of Trento, he worked at the IBM T.J. Watson Research Center. He has also been a Visiting Professor at the National University of Singapore, worked for the University of California, Riverside, and visited Microsoft Research and the IBM Almaden Research Center. His research solutions have been implemented in world-leading commercial data management products and he is the author of eight US patents, three of which are part of commercial products in multi-billion dollar markets. He is the recipient of three Best Paper awards. He has been a member of the IBM Academy of Technology Study on Event Processing, and is a founding member of the Event Processing Technical Society. He is General Chair for VLDB 2013, has served on the program committees of several top database and data mining conferences, and also serves as a reviewer for the European Commission Framework Programme, the Natural Sciences and Engineering Research Council of Canada (NSERC), the Netherlands Organisation for Scientific Research (NWO), and the Qatar National Research Fund (QNRF).

Slides
Themis Palpanas – Entity Resolution for Big Data

10.30, room 445, PCRI

Abstract
Web 2.0 technologies have transformed the Web from a publishing only environment into a vibrant information place where yesterday’s end users become nowadays content generators themselves. The vast amounts of user generated content available in various social media (Facebook, Twitter, blogs, discussion forums) in conjunction with traditional information producers (e.g., newspapers, television, radio) poses new challenges in achieving an effective, near real-time information awareness. In this talk we present recent results for supporting users to state their interests as continuous textual queries which will be matched on the fly against incoming information items originating from different sources. In particular, we will present in three complementary problems related to effective and efficient continuous filtering systems, namely (a) scalable indices for textual subscriptions in a Pub/Sub system (b) top-k continuous query evaluation with scoring functions (c) multi-query optimization for continuous textual mashups. The advocated solutions aim to meet the requirements for processing very large volumes of varying quality information published at different rates.

Short bio
Vassilis Christophides is Professor at the Computer Science Department (CSD) of the University of Crete. His main research interests include Databases and Web Information Systems, Digital Libraries and Scientific Systems. He has published over 100 articles in high-quality international conferences, workshops and journals. He has received the 2004 SIGMOD Test of Time Award and the Best Paper Award at the 2nd and 6th International Semantic Web Conference in 2003 & 2007.

Slides
Vassilis Christophides – Continuous Queries over Text Streams