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.
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.
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.
1786671
Light field compression using disparity-compensated lifting and shape adaptation
We propose disparity-compensated lifting for wavelet compression of light fields. With this approach, we obtain the benefits of wavelet coding, such as scalability in all dimensions, as well as superior compression performance. Additionally, the proposed approach solves the irreversibility limitations of previous light field wavelet coding approaches, using the lifting structure. Our scheme incorporates disparity compensation into the lifting structure for the transform across the views in the light field data set. Another transform is performed to exploit the coherence among neighboring pixels, followed by a modified SPIHT coder and rate-distortion optimized bitstream assembly. A view-sequencing algorithm is developed to organize the views for encoding. For light fields of an object, we propose to use shape adaptation to improve the compression efficiency and visual quality of the images. The necessary shape information is efficiently coded based on prediction from the existing geometry model. Experimental results show that the proposed scheme exhibits superior compression performance over existing light field compression techniques.
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.
11602397
Compiling process algebraic specifications into timed automata
The authors present LTP (Language of Timed Processes), a process-algebra-based specification language, for the specification of real-time applications. In addition to timing requirements involving the execution time of individual computations, LTP can specify timing constraints that are associated with process descriptions: periodic constraints and sporadic constraints. Periodic processes (e.g. sensors) are usually time-driven; sporadic processes are usually event-driven. In LTP common real-time constraints such as delays, deadlines, and timeouts can be modeled conveniently. They specifically introduce a new construct for modeling periodic behavior, which is quite common in real-time applications. They validate LTP specifications by translating from LTP to a kind of timed automata that is amenable to well-known model checking techniques. Although the models of LTP specifications, commonly known as timed transition systems, are usually infinite because of the presence of time, the translation results in a finite representation by which automatic verification is possible.
43825190
Temporal Relations and Synchronization Agents
Temporal relationships, inevitable among distributed multimedia objects, require synchronization of multiple data streams in time. We propose using synchronization agents as "smart" distributed objects to deal with scheduling, integrating, and synchronizing distributed multimedia streams. A formal specification model, interoperable Petri nets, describes the agents' behavior and captures the temporal semantics existing among distributed multimedia objects.
14595145
An Approach to Vision-Based Person Detection in Robotic Applications
We present an approach to vision-based person detection in robotic applications that integrates top down template matching with bottom up classifiers. We detect components of the human silhouette, such as torso and legs; this approach provides greater invariance than monolithic methods to the wide variety of poses a person can be in. We detect borders on each image, then apply a distance transform, and then match templates at different scales. This matching process generates a focus of attention (candidate people) that are later confirmed using a trained Support Vector Machine (SVM) classifier. Our results show that this method is both fast and precise and directly applicable in robotic architectures.