39549662
Categorical Data Visualization and Clustering Using Subjective Factors
Clustering is an important data mining problem. However, most earlier work on clustering focused on numeric attributes which have a natural ordering to their attribute values. Recently, clustering data with categorical attributes, whose attribute values do not have a natural ordering, has received more attention. A common issue in cluster analysis is that there is no single correct answer to the number of clusters, since cluster analysis involves human subjective judgement. Interactive visualization is one of the methods where users can decide a proper clustering parameters. In this paper, a new clustering approach called CDCS (Categorical Data Clustering with Subjective factors) is introduced, where a visualization tool for clustered categorical data is developed such that the result of adjusting parameters is instantly reflected. The experiment shows that CDCS generates high quality clusters compared to other typical algorithms.
14995712
Applying Pattern Mining to Web Information Extraction
Information extraction (IE) from semi-structured Web documents is a critical issue for information integration systems on the Internet. Previous work in wrapper induction aim to solve this problem by applying machine learning to automatically generate extractors. For example, WIEN, Stalker, Softmealy, etc. However, this approach still requires human intervention to provide training examples. In this paper, we propose a novel idea to IE, by repeated pattern mining and multiple pattern alignment. The discovery of repeated patterns are realized through a data structure call PAT tree. In addition, incomplete patterns are further revised by pattern alignment to comprehend all pattern instances. This new track to IE involves no human effort and content-dependent heuristics. Experimental results show that the constructed extraction rules can achieves 97 percent extraction over fourteen popular search engines.
6352215
Reconfigurable Web wrapper agents for biological information integration
A variety of biological data is transferred and exchanged in overwhelming volumes on the World Wide Web. How to rapidly capture, utilize, and integrate the information on the Internet to discover valuable biological knowledge is one of the most critical issues in bioinformatics. Many information integration systems have been proposed for integrating biological data. These systems usually rely on an intermediate software layer called wrappers to access connected information sources. Wrapper construction for Web data sources is often specially hand coded to accommodate the differences between each Web site. However, programming a Web wrapper requires substantial programming skill, and is time-consuming and hard to maintain. In this article we provide a solution for rapidly building software agents that can serve as Web wrappers for biological information integration. We define an eXtensible Markup Language (XML)-based language called Web Navigation Description Language (WNDL), to model a Web-browsing session. A WNDL script describes how to locate the data, extract the data, and combine the data. By executing different WNDL scripts, we can automate virtually all types of Web-browsing sessions. We also describe IEPAD (Information Extraction Based on Pattern Discovery), a data extractor based on pattern discovery techniques. IEPAD allows our software agents to automatically discover the extraction rules to extract the contents of a structurally formatted Web page. With a programming-by-example authoring tool, a user can generate a complete Web wrapper agent by browsing the target Web sites. We built a variety of biological applications to demonstrate the feasibility of our approach.
12151320
Mining Periodic Patterns in Sequence Data
Periodic pattern mining is the problem that regards temporal regularity. There are many emerging applications in periodic pattern mining, including web usage recommendation, weather prediction, computer networks and biological data. In this paper, we propose a Progressive Timelist-Based Verification (PTV) method to the mining of periodic patterns from a sequence of event sets. The parameter min_rep, is employed to specify the minimum number of repetitions required for a valid segment of non-disrupted pattern occurrences. We also describe a partitioning approach to handle extra large/long data sequence. The experiments demonstrate good performance and scalability with large frequent patterns.
5203884
Reconfigurable Web Wrapper Agents for Web Information Integration
This paper provides a solution for rapidly building software agents that can serve as Web wrappers for biological information integration. We define an XML-based language called WNDL, which provides a representation of a Web browsing session. A WNDL script describes how to locate the data, extract the data and combine the data. By executing different WNDL scripts, user can automate virtually all types of Web browsing sessions. We also describe IEPAD, a data extractor based on pattern discovery techniques. IEPAD allows our software agents to automatically discover the extraction rules to extract the contents of a structurally formatted Web page without the need to label a Web page to train a wrapper. With a programmingby-example authoring tool, a user can generate a complete Web wrapper agent by browsing the target Web sites. We have built a variety of applications to demonstrate the feasibility of our approach.
31867607
Reconfigurable Web Wrapper Agents
Web wrapper agents exploit online Web data sources, facilitating information integration and reuse. With the DeepSpot Agent Toolbox, users can automate virtually all types of Web browsing sessions simply by browsing the target Web sites.
18643244
An Empirical Performance Study on PSIM
It is well known that errors caught in the early stages of system development life cycle greatly helped reduce the expense for later error fixing and mitigate risks and complications. Performance analysis is one of the methods to ensure satisfactory system performance that can avoid redesign and patch in the later stage of system development when performance problems emerge. Much work has been done to address such concerns collectively known as the 'performance engineering' problem. However, a comprehensive method is still needed to provide an end-to-end support for performance evaluation on software architecture design. Automated tools to support the method must be developed, and its validation must be carefully planned and conducted. In response to this need, we propose our approach, PSIM (performance simulation and modeling). PSIM is a performance simulation and modeling tool that integrated performance properties into software architecture specifications expressed in several major UML diagrams. PSIM models can be transformed into Colored GSPN (colored generalized stochastic Petri nets) via an automated tool. As a result, the Colored GSPN models can be simulated to perform model-based performance evaluation. In this paper PSIM is first briefly reviewed and illustrated through modeling a web-based electronic conferencing system, called M-Net, to derive performance metrics. We then conduct runtime performance testing to the implementation of M-Net and compare the simulation results against the runtime testing data. PSIM is shown to be effective in predicting system performance and in identifying system performance bottlenecks through this experimentation.
33610290
Biomimetic Robotics
33039704
A circular skip-cluster scheme to support video-on-demand services
Abstract. The major drawback of the existing cluster placement scheme is the long response time caused by admission control if the number of clusters and the number of users are large. A circular skip-cluster placement scheme is proposed to reduce the size of the data buffer as well as the system response time. Furthermore, the popularity of each video is different in the real world. We propose a new popularity-based data allocation scheme to allocate data units within a cluster such that the corresponding data units of these popular videos are stored in those cylinders at one end of each cluster. Due to a higher spatial locality within these hot cylinders, some data units requested by the users are stored in the same cylinder such that one seek operation, one rotation, and one transfer operation are required to retrieve these data units. Therefore, the time required to retrieve data for these requests can be reduced, thus also reducing the system response time. Based on our results, the buffer size and the system response time can be reduced by half or more. These findings are essential for constructing video-on-demand systems that provide satisfactory performance.
36323277
Event-Based Traceability for Managing Evolutionary Change
Although the benefits of requirements traceability are widely recognized, the actual practice of maintaining a traceability scheme is not always entirely successful. The traceability infrastructure underlying a software system tends to erode over its lifetime, as time-pressured practitioners fail to consistently maintain links and update impacted artifacts each time a change occurs, even with the support of automated systems. This paper proposes a new method of traceability based upon event-notification and is applicable even in a heterogeneous and globally distributed development environment. Traceable artifacts are no longer tightly coupled but are linked through an event service, which creates an environment in which change is handled more efficiently, and artifacts and their related links are maintained in a restorable state. The method also supports enhanced project management for the process of updating and maintaining the system artifacts.
8773045
Characterizing and Exploiting Reference Locality in Data Stream Applications
In this paper, we investigate a new approach to process queries in data stream applications. We show that reference locality characteristics of data streams could be exploited in the design of superior and flexible data stream query processing techniques. We identify two different causes of reference locality: popularity over long time scales and temporal correlations over shorter time scales. An elegant mathematical model is shown to precisely quantify the degree of those sources of locality. Furthermore, we analyze the impact of locality-awareness on achievable performance gains over traditional algorithms on applications such asMAX-subset approximate sliding window join and approximate count estimation. In a comprehensive experimental study, we compare several existing algorithms against our locality-aware algorithms over a number of real datasets. The results validate the usefulness and efficiency of our approach.