12237012
Discovering the Biomedical Deep Web
The rapid growth of biomedical information in the Deep Web has produced unprecedented challenges for traditional search engines. This paper describes a new Deep web resource discovery system for biomedical information. We designed two hypertext mining applications: a Focused Crawler that selectively seeks out relevant pages using a classifier that evaluates the relevance of the document with respect to biomedical information, and a Query Interface Extractor that extracts information from the page to detect the presence of a Deep Web database. Our anecdotes suggest that combining focused crawling with query interface extraction is very effective for building high-quality collections of Deep Web resources on biomedical topics.
18594508
An index structure for efficient reverse nearest neighbor queries
The Reverse Nearest Neighbor (RNN) problem is to find all points in a given data set whose nearest neighbor is a given query point. Just like the Nearest Neighbor (NN) queries, the RNN queries appear in many practical situations such as marketing and resource management. Thus, efficient methods for the RNN queries in databases are required. The paper introduces a new index structure, the Rdnn-tree, that answers both RNN and NN queries efficiently. A single index structure is employed for a dynamic database, in contrast to the use of multiple indexes in previous work. This leads to significant savings in dynamically maintaining the index structure. The Rdnn-tree outperforms existing methods in various aspects. Experiments on both synthetic and real world data show that our index structure outperforms previous methods by a significant margin (more than 90% in terms of number of leaf nodes accessed) in RNN queries. It also shows improvement in NN queries over standard techniques. Furthermore, performance in insertion and deletion is significantly enhanced by the ability to combine multiple queries (NN and RNN) in one traversal of the tree. These facts make our index structure extremely preferable in both static and dynamic cases.
15536346
An Index Structure for Data Mining and Clustering
Abstract. In this paper we present an index structure, called MetricMap, that takes a set of objects and a distance metric and then maps those objects to a k-dimensional space in such a way that the distances among objects are approximately preserved. The index structure is a useful tool for clustering and visualization in data-intensive applications, because it replaces expensive distance calculations by sum-of-square calculations. This can make clustering in large databases with expensive distance metrics practical. We compare the index structure with another data mining index structure, FastMap, recently proposed by Faloutsos and Lin, according to two criteria: relative error and clustering accuracy. For relative error, we show that (i) FastMap gives a lower relative error than MetricMap for Euclidean distances, (ii) MetricMap gives a lower relative error than FastMap for non-Euclidean distances (i.e., general distance metrics), and (iii) combining the two reduces the error yet further. A similar result is obtained when comparing the accuracy of clustering. These results hold for different data sizes. The main qualitative conclusion is that these two index structures capture complementary information about distance metrics and therefore can be used together to great benefit. The net effect is that multi-day computations can be done in minutes.
17442336
Ordinal association rules for error identification in data sets
A new extension of the Boolean association rules, ordinal association rules, that incorporates ordinal relationships among data items, is introduced. One use for ordinal rules is to identify possible errors in data. A method that finds these rules and identifies potential errors in data is proposed.
18266651
A similarity-based soft clustering algorithm for documents
Document clustering is an important tool for applications such as Web search engines. Clustering documents enables the user to have a good overall view of the information contained in the documents that he has. However, existing algorithms suffer from various aspects, hard clustering algorithms (where each document belongs to exactly one cluster) cannot detect the multiple themes of a document, while soft clustering algorithms (where each document can belong to multiple clusters) are usually inefficient. We propose SISC (similarity-based soft clustering), an efficient soft clustering algorithm based on a given similarity measure. SISC requires only a similarity measure for clustering and uses randomization to help make the clustering efficient. Comparison with existing hard clustering algorithms like K-means and its variants shows that SISC is both effective and efficient.
46019766
A Study on Textual Entailment
In this paper we study a graph-based approach to the task of textual entailment between a text and hypothesis. The approach takes into account the full lexico-syntactic context of both the text and hypothesis and relies heavily on the concept of subsumption. It starts with mapping the text and hypothesis into graph structures where nodes represent concepts and edges represent lexico-syntactic relations among concepts. Based on a subsumption score between the text-graph and hypothesis-graph an entailment decision is then made. The impact of synonymy on entailment is quantified and discussed. An important advantage of our solution is the ability to customize it so that high-confidence results are obtained
9933111
Service-oriented computing
Service-oriented computing using Web services has emerged as a major research topic in recent years. Strong support from major computer companies including IBM, Microsoft, Hewlett-Packard, Oracle, and SAP has accelerated the acceptance and adoption of SOC using Web services. With SOC, enterprises can define and execute transactions across multiple business. Companies can use SOC's plug-and-play interoperability to compose business processes and integrate different information systems on the fly to enable ad hoc cooperation between new partners. Thus, SOC reinvents the way enterprises work together: common tasks in a business process or supply chain can be easily outsourced to external service providers for both performance and cost reasons.
40285763
Critical Points for Interactive Schema Matching
Experience suggests that fully automated schema matching is infeasible, especially for n-to-m matches involving semantic functions. It is therefore advisable for a matching algorithm not only to do as much as possible automatically, but also to accurately identify the critical points where user input is maximally useful. Our matching algorithm combines several existing approaches, with a new emphasis on using the context provided by the way elements are embedded in paths. A prototype tested on biological data (gene sequence, DNA, RNA, etc.) and on bibliographic data, shows significant performance improvements from user feedback and context checking. In non-interactive mode on the purchase order schemas, it compares favorably with COMA, the most mature schema matching system in literature, and also correctly identifies critical points for user input.
42000677
Real-Time Database Benchmark Design for Avionics Systems
Traditionally, database systems have been developed for data processing applications. With the development of CAD/CAM applications, object-oriented databases have been shown to be more flexible and effective in providing the kind of performance more adequate for CAD users. Recently, as a result of the rapid growth in embedded and autonomous systems applications, many database applications are moving toward the real-time domain that is defined by high data access and/or transaction execution rates as well as stringent timing constraints. These real-time characteristics require databases to have special capabilities to handle time-critical transactions. Some examples are databases for safety-critical applications like air traffic control, avionics systems, process control, and medical service systems.
206624023
A Class of Rate-Based Real-Time Scheduling Algorithms
This paper investigates a class of rate-based real-time scheduling algorithms based on the idea of general processor sharing (GPS). We extend the GPS framework of Parekh and Gallager (1993) for periodic and sporadic process scheduling and show the optimality of GPS-based scheduling. In particular, we propose the earliest-completion-time GPS (EGPS) scheduling algorithm to simulate the GPS algorithm with much lower run-time overheads. The schedulability of each process is enforced by a guaranteed CPU service rate, independent of the demands of other processes. We provide a theoretical foundation to assign proper CPU service rates to processes to satisfy their individual stringent response time requirements. We also propose a GPS-based scheduling mechanism for jitter control. Finally, the performance of the proposed algorithms is studied using a generic avionics platform example and simulation experiments on jitter control and mixed soft and hard real-time process scheduling.
10903877
WISE-building simple intelligence into Web services
Web services are self contained and self described modular applications that can be published, discovered and employed on the Web. Many standard protocols supporting Web services have been adopted and more are being proposed. We study the issue on providing intelligent Web services. We propose the enhancement of Web service functionalities by deploying software agents on both server side and/or client side. Our goal of designing the WISE Web service architecture is to provide a working middle ground between the current Web service standards and the semantic Web architecture. The WISE software agent architecture for Web services is presented. We discuss the design issues of WISE. We also present the QoS management protocol and algorithm that can be used by WISE servers.