Anant Jhingran

SemTag and seeker: bootstrapping the semantic web via automated semantic annotation

This paper describes Seeker, a platform for large-scale text analytics, and SemTag, an application written on the platform to perform automated semantic tagging of large corpora. We apply SemTag to a collection of approximately 264 million web pages, and generate approximately 434 million automatically disambiguated semantic tags, published to the web as a label bureau providing metadata regarding the 434 million annotations. To our knowledge, this is the largest scale semantic tagging effort to date.We describe the Seeker platform, discuss the architecture of the SemTag application, describe a new disambiguation algorithm specialized to support ontological disambiguation of large-scale data, evaluate the algorithm, and present our final results with information about acquiring and making use of the semantic tags. We argue that automated large scale semantic tagging of ambiguous content can bootstrap and accelerate the creation of the semantic web.

DB2 parallel edition

The rate of increase in database size and response-time requirements has outpaced advancements in processor and mass storage technology. One way to satisfy the increasing demand for processing power and input/output bandwidth in database applications is to have a number of processors, loosely or tightly coupled, serving database requests concurrently. Technologies developed during the last decade have made commercial parallel database systems a reality, and these systems have made an inroad into the stronghold of traditionally mainframe-based large database applications. This paper describes the DB2® Parallel Edition product that evolved from a prototype developed at IBM Research in Hawthorne, New York, and now is being jointly developed with the IBM Toronto laboratory.

A case for automated large-scale semantic annotation

Abstract This paper describes Seeker, a platform for large-scale text analytics, and SemTag, an application written on the platform to perform automated semantic tagging of large corpora. We apply SemTag to a collection of approximately 264 million web pages, and generate approximately 434 million automatically disambiguated semantic tags, published to the web as a label bureau providing metadata regarding the 434 million annotations. To our knowledge, this is the largest scale semantic tagging effort to date. We describe the Seeker platform, discuss the architecture of the SemTag application, describe a new disambiguation algorithm specialized to support ontological disambiguation of large-scale data, evaluate the algorithm, and present our final results with information about acquiring and making use of the semantic tags. We argue that automated large-scale semantic tagging of ambiguous content can bootstrap and accelerate the creation of the semantic web.

Information integration: A research agenda

The theme for this special issue--information integration--reflects the growing importance of integration in general, and data integration in particular, as a driving force in information technology spending. This essay discusses information integration along three axes--data types, federation, and intelligence. Several important problem areas are emerging--storage and retrieval of XML (Extensible Markup Language) documents, federation and distribution across data sources, and holistic intelligence across different data modalities. This special issue is devoted to papers on many of these topics, and we expect this to be an active area of research for many years to come.

Block oriented processing of relational database operations in modern computer architectures

Database systems are not well-tuned to take advantage of modern superscalar processor architectures. In particular, the clocks per instruction (CPI) for rather simple database queries are quite poor compared to scientific kernels or SPEC benchmarks. The lack of performance of database systems has been attributed to poor utilization of caches and processor function units as well as higher branching penalties. In this paper, we argue that a block-oriented processing strategy for database operations can lead to better utilization of the processors and caches, generating significantly higher performance. We have implemented the block-oriented processing technique for aggregation expression evaluation and sorting operations as a feature in the DB2 Universal Database (UDB) system. We present results from representative queries on a 30-GB TPC-H (Transaction Processing Council Benchmark H) database to show the value of this technique.

Dynamic assembly of views in data cubes

In this paper, we present a method for dynamically assembling views in multi-dimensional data cubes in order to more e ciently support data analysis and querying involving aggregations. The proposed method decomposes the data cubes into an indexed hierarchy of view elements. The view elements di er from traditional data cube cells in that they correspond to partial and residual aggregations of the data cube. The view elements provide highly granular building blocks for synthesizing the aggregated and rangeaggregated views of the data cubes. We propose a strategy for selecting and materializing the view elements based on the frequency of view access. This allows the dynamic adaptation of the view element sets to patterns of retrieval. We present a fast and optimal algorithm for selecting non-expansive view element sets that minimize the processing costs for generating a population of aggregated views. We also present a greedy algorithm for selecting redundant view element sets in order to further reduce processing costs. We demonstrate that the view element approaches perform better in terms of lower processing and storage costs than methods based on materializing views.

A wavelet framework for adapting data cube views for OLAP

This article presents a method for adaptively representing multidimensional data cubes using wavelet view elements in order to more efficiently support data analysis and querying involving aggregations. The proposed method decomposes the data cubes into an indexed hierarchy of wavelet view elements. The view elements differ from traditional data cube cells in that they correspond to partial and residual aggregations of the data cube. The view elements provide highly granular building blocks for synthesizing the aggregated and range-aggregated views of the data cubes. We propose a strategy for selectively materializing alternative sets of view elements based on the patterns of access of views. We present a fast and optimal algorithm for selecting a non-expansive set of wavelet view elements that minimizes the average processing cost for supporting a population of queries of data cube views. We also present a greedy algorithm for allowing the selective materialization of a redundant set of view element sets which, for measured increases in storage capacity, further reduces processing costs. Experiments and analytic results show that the wavelet view element framework performs better in terms of lower processing and storage cost than previous methods that materialize and store redundant views for online analytical processing (OLAP).

Moving up the food chain: supporting e-commerce applications on databases

Database systems have enjoyed a tremendous market because they have served many applications really well -- transaction processing in the beginning, and then decision support. Today, with over 200% cumulative growth rate in certain segments of E-Commerce, it is clear that this new class of applications will be a strong driver for databases to grow, commercially, as well as from a Research perspective. This paper outlines some of the issues that I have learnt in dealing with E-Commerce applications that may well be the focus of some of the research in database systems over the course of next few years.

Analysis of recovery in a database system using a write-ahead log protocol

In this paper we examine the recovery time in a database system using a Write-Ahead Log protocol, such as ARIES [9], under the assumption that the buffer replacement policy is strict LRU. In particular, analytical equations for log read time, data I/O, log application, and undo processing time are presented. Our initial model assumes a read/write ratio of one, and a uniform access pattern. This is later generalized to include different read/write ratios, as well as a “hot set” model (i.e. x% of the accesses go to y% of the data). We show that in the uniform access model, recovery is dominated by data I/O costs, but under extreme hot-set conditions, this may no longer be true. Furthermore, since we derive anaytical equations, recovery can be analyzed for any set of parameter conditions not discussed here.

Profit-driven matching in E-marketplaces: trading composable commodities

We are witnessing the dawn and emergence of anew breed of businesses over the Internet, such as electronic intermediaries, which are not only reducing search costs, but also aiming to create efficient markets for businesses and consumers to conduct commerce over the Internet. Among these intermediaries, Electronic Marketplaces are emerging to provide value by streamlining commerce among diverse buyers and sellers and creating fair trading environments. We envision that these E-Marketplaces will play a very significant role in matching buyers and sellers and increasing market efficiency by satisfying a variety of objectives ranging from pure profit maximization to guaranteeing a target market liquidity. In this paper, we consider a specific E-Marketplace for trading soft composable commodities such ass bandwidth and application quality of service, where buyers and sellers submit bids and offers to the marketplace to trade bandwidth products. We develop and analyze the performance of novel computationally efficient matching heuristics. The experimental results demonstrate that these heuristics perform as well as the exact computationally intensive matching algorithms.