2008 IEEE International Conference on Information Reuse and Integration最新文献

{"title":"Model check stochastic supply chains","authors":"Li Tan, Shenghan Xu","doi":"10.1109/IRI.2008.4583067","DOIUrl":"https://doi.org/10.1109/IRI.2008.4583067","url":null,"abstract":"Supply chain [2, 6] is an important component of business operations. Understanding its stochastic behaviors is the key to risk analysis and performance evaluation in supply chain design and management. We propose a novel computational framework for modeling and analyzing the stochastic behaviors of a supply chain. The framework is based on probabilistic model checking, a formal verification technique for analyzing stochastic systems. Our approach is two-fold: first, we develop Stochastic Merchandise Flow Model (SMF), a formal framework for modeling stochastic supply chains based on Extended Markov Decision Process (EMDP); second, we propose a model-checking-based formal technique to automate the analysis of a stochastic supply chain. Our model-checking-based approach leverages benefits of recent advances in symbolic probabilistic model checking to improve the efficiency and scalability of decision procedures. Using the temporal logic PCTL [1] and the symbolic probabilistic model checker PRISM [4], we are able to express and check complicate temporal and stochastic properties on supply chains. Finally, we demonstrate the capability of our model-checking-based approach by applying it to a variety of stochastic supply chain models.","PeriodicalId":169554,"journal":{"name":"2008 IEEE International Conference on Information Reuse and Integration","volume":"152 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121437108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Electronic learning support system based on analogy reuse","authors":"T. Matsuo, T. Fujimoto","doi":"10.1109/IRI.2008.4583054","DOIUrl":"https://doi.org/10.1109/IRI.2008.4583054","url":null,"abstract":"In design on a new teaching system, the challenging issues are including how the system intelligently supports learners. This paper describes a methodology and a system design on the intelligent instruction support in software engineering education by using the conception of human information reuse. To enhance learners’ understands, we design a novel instructional model based on the analogical thinking in AI-aided education. The analogical thinking-based instruction consists of three following concrete teaching methods, (1) Analogy dropping method, (2) Self role-play method, and (3) Anthropomorphic thinking method. Our proposed instruction system teaches learning issues based on them. Questionnaires for learners after instructions give the result of effective education in an actual trial. The contribution of this paper is to provide new education theory, the way of educational practice, and implementation of the system.","PeriodicalId":169554,"journal":{"name":"2008 IEEE International Conference on Information Reuse and Integration","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129038722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Compound record clustering algorithm for design pattern detection by decision tree learning","authors":"Jing Dong, Yongtao Sun, Yajing Zhao","doi":"10.1109/IRI.2008.4583034","DOIUrl":"https://doi.org/10.1109/IRI.2008.4583034","url":null,"abstract":"Recovering design patterns applied in a system can help refactoring the system. Machine learning algorithms have been successfully applied in mining data patterns. However, one of the main obstacles of applying them for design pattern detection is the difficulty of collecting training examples. Unlike other applications, a design pattern instance typically includes a group of classes with certain relationships. Thus, the possible combinations of the group of classes can be enormous which results in huge training sets making the application of machine learning algorithms impracticable. In this paper, we propose an innovative method using matrix transformations to cluster the training examples. Our method can significantly reduce the size of training examples, thus making it possible to be efficiently applied in machine learning algorithm.","PeriodicalId":169554,"journal":{"name":"2008 IEEE International Conference on Information Reuse and Integration","volume":"373 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124671098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust integration of multiple information sources by view completion","authors":"Shankara B. Subramanya, Baoxin Li, Huan Liu","doi":"10.1109/IRI.2008.4583064","DOIUrl":"https://doi.org/10.1109/IRI.2008.4583064","url":null,"abstract":"There are many applications where multiple data sources, each with its own features, are integrated in order to perform an inference task in an optimal way. Researchers have shown that for many tasks like webpage classification, image classification, and pattern recognition, combining data from multiple information sources yields significantly better results than using a single source. In these tasks each of the multiple data sources can be thought of as providing one view of the underlying object. However in many domains not all of the views are available for the available instances; some of the views would be missing. This problem of missing views affects the performance of the machine learning task. In this paper we provide a method of view completion to heuristically predict the missing views. We show that with view completion we are able to achieve significantly better results. We also show that by considering the information at a higher level in terms of views rather than considering them at a lower level in terms of features we are able to achieve better results. We demonstrate this by comparing our method with existing methods which consider the missing views problem as a missing value problem.","PeriodicalId":169554,"journal":{"name":"2008 IEEE International Conference on Information Reuse and Integration","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115737428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hierarchical affinity hybrid tree: A multidimensional index structure to organize videos and support content-based retrievals","authors":"Kasturi Chatterjee, Shu‐Ching Chen","doi":"10.1109/IRI.2008.4583070","DOIUrl":"https://doi.org/10.1109/IRI.2008.4583070","url":null,"abstract":"Multimedia data, especially videos, have gained enormous popularity in the recent years. Data management techniques for traditional text-based data are inadequate to handle multimedia data efficiently due to their atypical characteristics. Thus, to have a robust data management framework for complex multimedia data like videos, comparable in efficiency and capability to the traditional data management approaches, components like multimedia data storage, index, and query engines need to be developed with dedicated abilities to handle the characteristics of multimedia data like multidimensional representation and semantic gap. In this paper, we investigate the design of the second component, i.e., a multimedia index, and propose a novel tree-based multidimensional hierarchical index structure called Hierarchical Affinity Hybrid-Tree (HAH-Tree) which addresses the critical issues of multidimensionality and semantic gap. The index structure accommodates different levels of video relationships during Content-Based Video Retrieval (CBVR) by utilizing a probabilistic approach called the Hierarchical Markov Model Mediator (HMMM), which is also responsible for managing the high-level semantic content of the video components. In addition, a computationally efficient k-Nearest Neighbor (k-NN) algorithm is proposed, which supports CBVR for different video units with a high precision level.","PeriodicalId":169554,"journal":{"name":"2008 IEEE International Conference on Information Reuse and Integration","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133612108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exercise energy expenditure estimation based on acceleration data using the linear mixed model","authors":"E. Haapalainen, P. Laurinen, Pekka Siirtola, J. Röning, H. Kinnunen, H. Jurvelin","doi":"10.1109/IRI.2008.4583018","DOIUrl":"https://doi.org/10.1109/IRI.2008.4583018","url":null,"abstract":"This paper introduces a novel algorithm for estimating energy expenditure during physical activity. The estimation is based on acceleration data measured from a wrist-worn accelerometer. Simultaneous measurements of acceleration and oxygen consumption using a biaxial accelerometer and a breath gas analyzer were made during four different activities: walking, running, Nordic walking and bicycling. A variance feature is used to compress the original acceleration signals. A linear mixed model is fitted to the data to estimate oxygen consumption based on the acceleration data. Lagged values of acceleration are used to take the delayed effect of physical activity on oxygen consumption into consideration. The algorithm also uses information on the height of the subjects. Oxygen consumption is estimated at 15-second intervals and energy expenditure is directly calculated from the oxygen consumption. Based on the experimental data gathered from 10 subjects, a new algorithm for estimating energy expenditure is suggested. It is shown that the method estimates energy expenditure very accurately. In walking, running and Nordic walking the model underestimates energy expenditure by 13, 2 and 9 percent, respectively, and in bicycling energy expenditure is overestimated by 7 percent. Thus, the new approach is a very promising method for estimating energy expenditure.","PeriodicalId":169554,"journal":{"name":"2008 IEEE International Conference on Information Reuse and Integration","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132797204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A new agent characterization model and grouping method for multi-agent system","authors":"Hyun Ko, S. Han, U. Kim, H. Youn","doi":"10.1109/IRI.2008.4583010","DOIUrl":"https://doi.org/10.1109/IRI.2008.4583010","url":null,"abstract":"In ubiquitous environment a number of agents dynamically collaborate with each other. Therefore, it is very important to effectively model their characteristics and group them accordingly. In this paper we introduce a new agent modeling and grouping method based on the supplier/demander model. The degree of matching between the agents is modeled by UoP (Utility of Predicate), UoA (Utility of Agent), and UoC (Utility of Community). An experiment reveals that the proposed scheme drastically decreases useless data stream and inter-broker communications compared to random grouping.","PeriodicalId":169554,"journal":{"name":"2008 IEEE International Conference on Information Reuse and Integration","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130834917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing PPDM in asynchronous sparse data using random projection","authors":"R. R. Kumar, J. Indumathi, G. Uma","doi":"10.1109/IRI.2008.4583066","DOIUrl":"https://doi.org/10.1109/IRI.2008.4583066","url":null,"abstract":"Privacy is fetching a progressively more imperative issue in several data-mining applications dealing with sensitive data especially in health care, security, financial, behavioral etc., Most of the existing techniques are managing a Secure Two-Party Computation model, where two parties, each having a private database, want to cooperatively conduct data-mining operations on the union of their data. The problem we are pinning down for Privacy Preserving Data Mining(PPDM), is how a data owner can release a version of its confidential data with guarantees that the original sensitive information cannot be re-identified while the analytic properties of the data are preserved. In this paper we work to investigate the leeway of using multiplicative random projection sparse matrices for privacy preserving data in datasets which gets incremented asynchronously over time from various sources. The data stream is asynchronous. This work proposes the use of random projections with a sparse matrix to maintain a sketch of a collection of high-dimensional data-streams that are updated asynchronously. This sketch allows us to estimate L2 (Euclidean) distances and dot products with high accuracy. We have also proposed a conceptual architecture for implementing the privacy preservation techniques especially the Sparse Random Projection Matrix technique in incremental data to improve the level of privacy protection. We have tested to see that the perturbed data still preserves certain statistical characteristics of the data as the original unperturbed data. At this juncture we have proposed a generic projection based sketch for incremental data stream which can be used not only for this application but also can be used for any other applications, which supports incremental data bases. We have traced the origin of PPDM, the definition of privacy preservation in data mining, and the implications of benchmark privacy doctrine in information detection and advocate a few policies for PPDM based on these privacy principles. These are vital for the development and deployment of methodological solutions. This will let vendors and developers to construct unyielding information reuse and integration (IRI) in PPDM. We pursue to capitalize on the reuse of PPDM information by crafting easy, affluent, and reusable knowledge depictions and accordingly investigates tactics for amalgamate this knowledge into heritage systems and make advances in the upcoming of PPDM.","PeriodicalId":169554,"journal":{"name":"2008 IEEE International Conference on Information Reuse and Integration","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129244543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RFID composite event definition and detection","authors":"Omar Gonzalez-Padilla, F. Corchado, H. Unger","doi":"10.1109/IRI.2008.4583074","DOIUrl":"https://doi.org/10.1109/IRI.2008.4583074","url":null,"abstract":"RFID systems generate large volume of data about localization of people and objects; this information is filtered by a middleware and sent to upper level applications so they can detect events happening in the environment and react properly. This approach implies two drawbacks: firstly, developers invest time programming how to analyze data; secondly, network resources could be unnecessarily wasted when middleware sends data which is irrelevant for the application. To overcome these drawbacks, we present an approach where applications define composite events of interest through a XML-based language, and filtered information is analyzed by a new layer in order to notify applications only when interesting events occur. We present our language called RFID-CEDL for defining interesting events using RFID data and we describe the mechanism used to recognize such events. As demonstration of our approach, we present examples for a hospital environment.","PeriodicalId":169554,"journal":{"name":"2008 IEEE International Conference on Information Reuse and Integration","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114975423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Preventing customer churn by using random forests modeling","authors":"Weiyun Ying, Xiu Li, Yaya Xie, Ellis L. Johnson","doi":"10.1109/IRI.2008.4583069","DOIUrl":"https://doi.org/10.1109/IRI.2008.4583069","url":null,"abstract":"In this paper, we use the improved balanced random forests(IBRF) to predict the customer churn, while integrating a sampling technique and cost-sensitive learning into the standard random forests to achieve a better performance than most existing algorithms. The nature of IBRF is that the best features are iteratively learned by altering the class distribution and by putting higher penalties on misclassification of the minority class. Applied to a credit debt customer database of an anonymous commercial bank in China, they are proven to significantly improve prediction accuracy comparing with other algorithms, such as artificial neural networks, decision trees, and class-weighted core support vector machines (CWC-SVM). The assessment and comparison of these algorithms are made to analyze the traits of them. Data processing and sampling scheme are also detailed introduced.","PeriodicalId":169554,"journal":{"name":"2008 IEEE International Conference on Information Reuse and Integration","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121465095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}