Title: Providing Scalable Database Services on the Cloud

Speaker: Beng Chin Ooi

Abstract:
The Cloud is fast gaining popularity as a platform for deploying Software as a Service (SaaS) applications. In principle, the Cloud provides unlimited compute resources, enabling deployed services to scale seamlessly, and supports the pay-as-you-go model, enabling the overall running cost of the applications to be kept low. Given the advantages of the Cloud, it is attractive to migrate existing software to this new platform. However, challenges remain as most software applications need to be redesigned to embrace the Cloud.

In this talk, I will present an overview of our current on-going work in developing epiC – an elastic and efficient power-aware data-intensive Cloud system. I will discuss the design issues and the implementation of epiC’s storage system and processing engine. The storage system and the processing engine are loosely coupled, and have been designed to handle two types of workload simultaneously, namely data-intensive analytical jobs and online transactions (commonly referred as OLAP and OLTP respectively). The processing of large-scale analytical jobs in epiC adopts a phase-based processing strategy, which provides a fine-grained fault tolerance, while the processing of queries adopts indexing and filter-and-refine strategies.

Short Bio:
Beng Chin is Professor of Computer Science at School of Computing, at the National University of Singapore (NUS). He obtained his BSc (1st Class Honors) and PhD from Monash University, Australia, in 1985 and 1989 respectively. His research interests include database performance issues, indexing techniques, multimedia and spatio-temporal databases, P2P systems and advanced applications, and cloud computing. His current system projects include BestPeer, P2P based data management system, and epiC, a data-intensive cloud computing platform.

He has served as a PC member for international conferences including ACM SIGMOD, VLDB, IEEE ICDE, WWW, SIGKDD and Vice PC Chair for ICDE'00,04,06, co-PC Chair for SSD'93 and DASFAA'05, PC Chair for ACM SIGMOD'07, and Core DB track PC chair for VLDB'08. He is the Editor-in-Chief of IEEE Transactions on Knowledge and Data Engineering (TKDE), and a trustee member of VLDB Endowment Board. He is the recipient of ACM SIGMOD 2009 Contributions award.

Title: Search and Social Integration

Speaker: Edward Chang

Abstract:
Search and Social have been widely considered to be two separate applications. Indeed, most people use search engines and visit social sites to conduct vastly different activities. This talk presents opportunities where search and social can be integrated synergistically. For instance, on the one hand, a search engine can mine search history data to facilitate uses to engage in social activities. On the other hand, user activities at social sites can provide information for search engines to improve personalized targeting. This talk uses Confucius, a Q&A system which Google develops and has launched in more than 20 countries, to illustrate how computer algorithms can assist synergistic integration between search and social. Algorithmic issues in data mining, information ranking, and system scalability are discussed.

Short Bio:
Edward Chang heads Google Research in China since March 2006. He joined the department of Electrical & Computer Engineering at University of California, Santa Barbara, in 1999 after receiving his PhD from Stanford University. Ed received his tenure in 2003, and was promoted to full professor of Electrical Engineering in 2006. His recent research activities are in the areas of distributed data mining and their applications to rich-media data management and social-network collaborative filtering. His research group (which consists of members from Google, UC, MIT, Tsinghua, PKU, and Zheda) recently parallelized SVMs (NIPS 07), PLSA (KDD 08), Association Mining (ACM RS 08), Spectral Clustering (ECML 08), and LDA (WWW 09) (see MMDS/CIVR/EMMDS/MM/AAIM/ADMA/CIKM keynotes and tutorials for details) to run on thousands of machines for mining large-scale datasets. His team at Google developed and launched Google Confucius (a Q&A system) at China, Russia, Thailand, and 17 Arabic countries. Ed has served on ACM (SIGMOD, KDD, MM, CIKM), VLDB, IEEE, WWW, and SIAM conference program committees, and co-chaired several conferences including MMM, ACM MM, ICDE, and WWW. Ed is a recipient of the IBM Faculty Partnership Award and the NSF Career Award.

Title: Elements of a Spatial Web

Speaker: Christian S. Jensen

Abstract:
Driven by factors such as the increasingly mobile use of the web and the proliferation of geo-positioning technologies, the web is rapidly acquiring a spatial aspect. Specifically, content and users are being geo-tagged, and services are being developed that exploit these tags. The research community is hard at work inventing means of efficiently supporting new spatial query functionality.

Points of interest with a web presence, called spatial web objects, have a location as well as a textual description. Spatio-textual queries return such objects that are near a location argument and are relevant to a text argument. An important element in enabling such queries is to be able to rank spatial web objects. Another is to be able to determine the relevance of an object to a query. Yet another is to enable the efficient processing of such queries. The talk covers recent results on spatial web object ranking and spatio-textual querying obtained by the speaker and his colleagues.

Short Bio:
Christian S. Jensen is a Professor of Computer Science at Aarhus University, Denmark. His research concerns data management and spans semantics, modeling, and indexing and query processing. Since the mid-90s, his focus has been on spatio-temporal data management. He is an IEEE Fellow and a member of the Royal Danish Academy of Sciences and Letters, the Danish Academy of Technical Sciences, the EDBT Endowment, and ERCIM's Board of Directors. He is vice chair of ACM SIGMOD and a trustee emeritus of the VLDB Endowment. He received the Ib Henriksen Research Award 2001 for his research in mainly temporal data management and Telenor's Nordic Research Award 2002 for his research in mobile services.

He is an editor-in-chief of The VLDB Journal and has served on the editorial boards of ACM TODS, IEEE TKDE, and the IEEE Data Engineering Bulletin. He was PC chair or co-chair for SSTD 2001, EDBT 2002, VLDB 2005, MobiDE 2006, MDM 2007, DMSN 2008, and TIME 2008. He will be PC chair for ACM SIGSPATIAL GIS 2011.

He recently spent a 1-year sabbatical at Google Inc., Mountain View.

Title: The Ubiquitous DBMS

Speaker: Kyu-Young Whang

Abstract:
Recent widespread use of mobile technologies and advancement in computing power prompted strong needs of database systems that can be used in small devices such as sensors, cellular phones, PDA, ultra PCs, and navigators. We call database systems that are customizable from small-scale applications for small devices to large-scale applications such as large-scale search engines ubiquitous database management systems (UDBMSs). In this talk, we first review requirements of UDBMSs. The requirements we identified include selective convergence (or “devicetization”), flash-optimized storage systems, data synchronization, supportability of unstructured/semi-structured data, and complex database operations. We then review existing systems and research prototypes. We first review the functionality of UDBMSs including the footprint size, support of standard SQL, supported data types, transactions, concurrency control, indexing, and recovery. We then review the supportability of requirements by those UDBMSs surveyed. We highlight ubiquitous features of a family of Odysseus systems that have been under development at KAIST for over 20 years. Functionalities of Odysseus can be “devicetized” or customized depending on the device types and applications as in Odysseus/Mobile for small devices, Odysseus/XML for unstructured/semistructured data, Odysseus/GIS for map data, and Odysseus/IR for large-scale search engines. We finally present research topics that are related to the UDBMSs.

Short Bio:
Kyu-Young Whang is a KAIST Distinguished Professor and Professor of Computer Science at KAIST. Previously, he was with IBM T.J.Watson Research Center from 1983 to 1990. Since joining KAIST in 1990, he has been leading the Odysseus DBMS/Search Engine project featuring tight-coupling of DBMS with information retrieval (IR) and spatial functions. An earlier version of this technology played a vital role in starting up NaverCom Co. (currently, NHN Co.) in 1997-2000, which currently is the number one portal in Korea. Dr. Whang is one of the pioneers of probabilistic counting, which nowadays is being widely used in approximate query answering, sampling, and data streaming. One of the algorithms he co-developed at IBM Almaden (then San Jose) Research Lab in 1981 has been made part of DB2. Dr. Whang is the author of the first main-memory relational query optimization model developed in 1985 and reported in 1990 in ACM TODS in the context of Office-by-Example (OBE). This model influenced subsequent optimization models of commercial main-memory DBMSs. His research has covered a wide range of database issues including physical database design, query optimization, DBMS engine technologies, and more recently, IR, spatial databases, data mining, and XML. Dr. Whang was the Coordinating Editor-in-Chief of the prestigious VLDB Journal, having served the journal for 19 years from its inception as a founding editorial board member. He is a re-elected Trustee of the VLDB Endowment (having served the first term from 1998 to 2004) and served the international academic community as the General Chair of VLDB2006, DASFAA2004, and PAKDD2003, as a PC Co-Chair of VLDB2000, CoopIS1998, and ICDE2006, and as an editorial board member of journals such as IEEE TKDE, The WWW Journal, and IEEE Data Engineering Bulletin. He served as the Chair of the Steering Committee of the DASFAA International Conference and as a co-founder of the Korea-Japan Database Workshop (KJDB) annually held alternately in Korea and Japan. He is a member of the ACM SIGMOD Dissertation Award Committee and served as a member of many 10-year Best or Influential Paper Award committees of VLDB and IEEE ICDE. He served as an IEEE Distinguished Visitor from 1989 to 1990 and was invited to ACM SIGMOD Distinguished Profile in Databases in 2007. He earned his Ph.D. from Stanford University in 1984. Dr. Whang is an ACM Fellow, an IEEE Fellow, and a member of IFIP WG 2.6.