Pub Date : 2002-11-04DOI: 10.1109/TAI.2002.1180811
Justin S. Di Stefano, T. Menzies
There are many machine learning algorithms currently available. In the 21st century, the problem no longer lies in writing the learner but in choosing which learners to run on a given data set. We argue that the final choice of learners should not be exclusive; in fact, there are distinct advantages in running data sets through multiple learners. To illustrate our point, we perform a case study on a reuse data set using three different styles of learners: association rule, decision tree induction, and treatment. Software reuse is a topic of avid debate in the professional and academic arena; it has proven that it can be both a blessing and a curse. Although there is much debate over where and when reuse should be instituted into a project, our learners found some procedures which should significantly improve the odds of a reuse program succeeding.
{"title":"Machine learning for software engineering: case studies in software reuse","authors":"Justin S. Di Stefano, T. Menzies","doi":"10.1109/TAI.2002.1180811","DOIUrl":"https://doi.org/10.1109/TAI.2002.1180811","url":null,"abstract":"There are many machine learning algorithms currently available. In the 21st century, the problem no longer lies in writing the learner but in choosing which learners to run on a given data set. We argue that the final choice of learners should not be exclusive; in fact, there are distinct advantages in running data sets through multiple learners. To illustrate our point, we perform a case study on a reuse data set using three different styles of learners: association rule, decision tree induction, and treatment. Software reuse is a topic of avid debate in the professional and academic arena; it has proven that it can be both a blessing and a curse. Although there is much debate over where and when reuse should be instituted into a project, our learners found some procedures which should significantly improve the odds of a reuse program succeeding.","PeriodicalId":197064,"journal":{"name":"14th IEEE International Conference on Tools with Artificial Intelligence, 2002. (ICTAI 2002). Proceedings.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2002-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113984276","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}
Pub Date : 2002-11-04DOI: 10.1109/TAI.2002.1180833
J. Tracy, LiWu Chang, I. S. Moskowitz
We propose an inference prevention agent as a tool that enables each of the databases in a distributed system to keep track of probabilistic dependencies with other databases and then use that information to help preserve the confidentiality of sensitive data. This is accomplished with minimal sacrifice of the performance and survivability gains that are associated with distributed database systems.
{"title":"An agent-based approach to inference prevention in distributed database systems","authors":"J. Tracy, LiWu Chang, I. S. Moskowitz","doi":"10.1109/TAI.2002.1180833","DOIUrl":"https://doi.org/10.1109/TAI.2002.1180833","url":null,"abstract":"We propose an inference prevention agent as a tool that enables each of the databases in a distributed system to keep track of probabilistic dependencies with other databases and then use that information to help preserve the confidentiality of sensitive data. This is accomplished with minimal sacrifice of the performance and survivability gains that are associated with distributed database systems.","PeriodicalId":197064,"journal":{"name":"14th IEEE International Conference on Tools with Artificial Intelligence, 2002. (ICTAI 2002). Proceedings.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2002-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124224729","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}
Pub Date : 2002-11-04DOI: 10.1109/TAI.2002.1180793
Chi-Hung Chi, Chen Ding, Kwok-Yan Lam
There is an important query requirement missing for search engines. With the wide variation of domain knowledge and user interest, a user would like to retrieve documents in which one query term is discussed in the context of another. Based on existing query mechanisms, what can be specified at most is the co-occurrence of multiple terms in a query. This is insufficient because the co-occurrence of two terms does not necessarily mean that one is discussed in the context of the other. In this paper we propose the context query for Web searching. A new query operator, called the 'in' operator, is used to specify context inclusion between two terms. Heuristic rules to identify context inclusion are suggested and implementation of the 'in' operator in search engines is proposed. Results show that both the precision and ranking relevance of Web searching are improved significantly.
{"title":"Context query in information retrieval","authors":"Chi-Hung Chi, Chen Ding, Kwok-Yan Lam","doi":"10.1109/TAI.2002.1180793","DOIUrl":"https://doi.org/10.1109/TAI.2002.1180793","url":null,"abstract":"There is an important query requirement missing for search engines. With the wide variation of domain knowledge and user interest, a user would like to retrieve documents in which one query term is discussed in the context of another. Based on existing query mechanisms, what can be specified at most is the co-occurrence of multiple terms in a query. This is insufficient because the co-occurrence of two terms does not necessarily mean that one is discussed in the context of the other. In this paper we propose the context query for Web searching. A new query operator, called the 'in' operator, is used to specify context inclusion between two terms. Heuristic rules to identify context inclusion are suggested and implementation of the 'in' operator in search engines is proposed. Results show that both the precision and ranking relevance of Web searching are improved significantly.","PeriodicalId":197064,"journal":{"name":"14th IEEE International Conference on Tools with Artificial Intelligence, 2002. (ICTAI 2002). Proceedings.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2002-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129037679","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}
Pub Date : 2002-11-04DOI: 10.1109/TAI.2002.1180844
Felix Freitag, Jordi Caubet, Jesús Labarta
Tracing and performance analysis tools are an important component in the development of high performance applications. Tracing parallel programs with current tracing tools, however, easily leads to large trace files with hundreds of Megabytes. The storage, visualization, and analysis of such trace files is often difficult. We propose a trace-scaling agent for tracing parallel applications, which learns the application behavior in runtime and achieves a small, easy to handle trace. The agent dynamically identifies the amount of information needed to capture the application behavior. This knowledge acquired at runtime allows recording only the non-iterative trace information, which drastically reduces the size of the trace file.
{"title":"A trace-scaling agent for parallel application tracing","authors":"Felix Freitag, Jordi Caubet, Jesús Labarta","doi":"10.1109/TAI.2002.1180844","DOIUrl":"https://doi.org/10.1109/TAI.2002.1180844","url":null,"abstract":"Tracing and performance analysis tools are an important component in the development of high performance applications. Tracing parallel programs with current tracing tools, however, easily leads to large trace files with hundreds of Megabytes. The storage, visualization, and analysis of such trace files is often difficult. We propose a trace-scaling agent for tracing parallel applications, which learns the application behavior in runtime and achieves a small, easy to handle trace. The agent dynamically identifies the amount of information needed to capture the application behavior. This knowledge acquired at runtime allows recording only the non-iterative trace information, which drastically reduces the size of the trace file.","PeriodicalId":197064,"journal":{"name":"14th IEEE International Conference on Tools with Artificial Intelligence, 2002. (ICTAI 2002). Proceedings.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2002-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116849700","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}
Pub Date : 2002-11-04DOI: 10.1109/TAI.2002.1180802
H. Vafaie, M. Shaughnessy, T. Bethem, J. Burton
The decision support tool market has seen exponential growth in recent years, with the introduction of different tools for various applications and domains. These include tools for data mining, on-line analysis and reporting, and expert systems (rule-based and case-based systems). This paper presents an approach for evaluating and selecting intelligent decision supports tools suitable for capturing expert's knowledge and deploying this knowledge where applicable.
{"title":"Selection process of intelligent decision support tool for real-time monitoring system","authors":"H. Vafaie, M. Shaughnessy, T. Bethem, J. Burton","doi":"10.1109/TAI.2002.1180802","DOIUrl":"https://doi.org/10.1109/TAI.2002.1180802","url":null,"abstract":"The decision support tool market has seen exponential growth in recent years, with the introduction of different tools for various applications and domains. These include tools for data mining, on-line analysis and reporting, and expert systems (rule-based and case-based systems). This paper presents an approach for evaluating and selecting intelligent decision supports tools suitable for capturing expert's knowledge and deploying this knowledge where applicable.","PeriodicalId":197064,"journal":{"name":"14th IEEE International Conference on Tools with Artificial Intelligence, 2002. (ICTAI 2002). Proceedings.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2002-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123326252","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}
Pub Date : 2002-11-04DOI: 10.1109/TAI.2002.1180827
K. Karimi, Howard J. Hamilton
Discovering causal and temporal relations in a system is essential to understanding how it works, and to learning to control the behaviour of the system. TimeSleuth is a causality miner that uses association relations as the basis for the discovery of causal and temporal relations. It does so by introducing time into the observed data. TimeSleuth uses C4.5 as its association discoverer, and by using a series of preprocessing and post-processing techniques to enable the user to try different scenarios for mining causality. The data to be mined should originate sequentially from a single system. TimeSleuth's use of a standard decision tree builder such as C4.5 puts it outside the current mainstream method of discovering causality, which is based on conditional independencies and causal Bayesian networks. This paper introduces TimeSleuth as a tool, and describes its functionality. It is an unsupervised tool that can handle and interpret temporal data. It also helps the user in analyzing the relationships among the attributes. There is also a mechanism to distinguish between causality and acausal relations. The user is thus encouraged to perform experiments and discover the nature of relationships among the data.
{"title":"TimeSleuth: a tool for discovering causal and temporal rules","authors":"K. Karimi, Howard J. Hamilton","doi":"10.1109/TAI.2002.1180827","DOIUrl":"https://doi.org/10.1109/TAI.2002.1180827","url":null,"abstract":"Discovering causal and temporal relations in a system is essential to understanding how it works, and to learning to control the behaviour of the system. TimeSleuth is a causality miner that uses association relations as the basis for the discovery of causal and temporal relations. It does so by introducing time into the observed data. TimeSleuth uses C4.5 as its association discoverer, and by using a series of preprocessing and post-processing techniques to enable the user to try different scenarios for mining causality. The data to be mined should originate sequentially from a single system. TimeSleuth's use of a standard decision tree builder such as C4.5 puts it outside the current mainstream method of discovering causality, which is based on conditional independencies and causal Bayesian networks. This paper introduces TimeSleuth as a tool, and describes its functionality. It is an unsupervised tool that can handle and interpret temporal data. It also helps the user in analyzing the relationships among the attributes. There is also a mechanism to distinguish between causality and acausal relations. The user is thus encouraged to perform experiments and discover the nature of relationships among the data.","PeriodicalId":197064,"journal":{"name":"14th IEEE International Conference on Tools with Artificial Intelligence, 2002. (ICTAI 2002). Proceedings.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2002-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131842541","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}
Pub Date : 2002-11-04DOI: 10.1109/TAI.2002.1180814
F. Ber, A. Napoli
This paper is concerned with the representation and classification of spatial relations and structures in an object-based knowledge representation system. In this system, spatial structures are defined as sets of spatial entities connected with topological relations. Relations are represented by objects with their own properties. We propose to define two types of properties: the first are concerned with relations as concepts while the second are concerned with relations as links between concepts. In order to represent the second type of properties, we have defined facets that are inspired from the constructors of description logics. We describe these facets and how they are used for classifying spatial structures and relations on land-use maps. Links between the present work and related work in description logics are also discussed.
{"title":"Object-based representation and classification of spatial structures and relations","authors":"F. Ber, A. Napoli","doi":"10.1109/TAI.2002.1180814","DOIUrl":"https://doi.org/10.1109/TAI.2002.1180814","url":null,"abstract":"This paper is concerned with the representation and classification of spatial relations and structures in an object-based knowledge representation system. In this system, spatial structures are defined as sets of spatial entities connected with topological relations. Relations are represented by objects with their own properties. We propose to define two types of properties: the first are concerned with relations as concepts while the second are concerned with relations as links between concepts. In order to represent the second type of properties, we have defined facets that are inspired from the constructors of description logics. We describe these facets and how they are used for classifying spatial structures and relations on land-use maps. Links between the present work and related work in description logics are also discussed.","PeriodicalId":197064,"journal":{"name":"14th IEEE International Conference on Tools with Artificial Intelligence, 2002. (ICTAI 2002). Proceedings.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2002-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121141134","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}
Pub Date : 2002-11-04DOI: 10.1109/TAI.2002.1180803
E. Liu, Du Zhang
Although the Space Shuttle is a high reliability system, its condition must he accurately diagnosed in real-time. Two problems plague the system - false alarms that may be costly, and missed alarms which may be not only expensive, but also dangerous to the crew. This paper describes the results of a feasibility study in which a multivariate state estimation technique is coupled with a Bayesian belief network to provide both fault detection and fault diagnostic capabilities for the Space Shuttle main engines (SSME). Five component failure modes and several single sensor failures are simulated in our study and correctly diagnosed. The results indicate that this is a feasible fault detection and diagnosis technique and fault detection and diagnosis can he made earlier than standard redline methods allow.
{"title":"Diagnosis of component failures in the Space Shuttle main engines using Bayesian belief networks: a feasibility study","authors":"E. Liu, Du Zhang","doi":"10.1109/TAI.2002.1180803","DOIUrl":"https://doi.org/10.1109/TAI.2002.1180803","url":null,"abstract":"Although the Space Shuttle is a high reliability system, its condition must he accurately diagnosed in real-time. Two problems plague the system - false alarms that may be costly, and missed alarms which may be not only expensive, but also dangerous to the crew. This paper describes the results of a feasibility study in which a multivariate state estimation technique is coupled with a Bayesian belief network to provide both fault detection and fault diagnostic capabilities for the Space Shuttle main engines (SSME). Five component failure modes and several single sensor failures are simulated in our study and correctly diagnosed. The results indicate that this is a feasible fault detection and diagnosis technique and fault detection and diagnosis can he made earlier than standard redline methods allow.","PeriodicalId":197064,"journal":{"name":"14th IEEE International Conference on Tools with Artificial Intelligence, 2002. (ICTAI 2002). Proceedings.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2002-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133733480","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}
Pub Date : 2002-11-04DOI: 10.1109/TAI.2002.1180808
A. Lim, B. Rodrigues, Fei Xiao, Xingwen Zhang
In this paper, we study shelf space allocation optimization which is important to retail operations management. Our approach is to formulate a model that is applicable to operational realities and to seek solutions with realistic test data. This model is linked to the multidimensional knapsack problem. We first solve a simplified version of the problem to achieve maximum profit by transforming it into a network flow problem. Then, with simple adaptations we solve the general shelf space allocation problem with the help of the network flow model. The approach is simple and direct while experimental results improve on recent findings significantly and are very close to the optimal.
{"title":"Adjusted network flow for the shelf-space allocation problem","authors":"A. Lim, B. Rodrigues, Fei Xiao, Xingwen Zhang","doi":"10.1109/TAI.2002.1180808","DOIUrl":"https://doi.org/10.1109/TAI.2002.1180808","url":null,"abstract":"In this paper, we study shelf space allocation optimization which is important to retail operations management. Our approach is to formulate a model that is applicable to operational realities and to seek solutions with realistic test data. This model is linked to the multidimensional knapsack problem. We first solve a simplified version of the problem to achieve maximum profit by transforming it into a network flow problem. Then, with simple adaptations we solve the general shelf space allocation problem with the help of the network flow model. The approach is simple and direct while experimental results improve on recent findings significantly and are very close to the optimal.","PeriodicalId":197064,"journal":{"name":"14th IEEE International Conference on Tools with Artificial Intelligence, 2002. (ICTAI 2002). Proceedings.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2002-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122392673","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}
Pub Date : 2002-11-04DOI: 10.1109/TAI.2002.1180789
Yixin Chen, B. Wah
We propose new dominance relations that can speed up significantly the solution process of nonlinear constrained dynamic optimization problems in discrete time and space. We first show that path dominance in dynamic programming cannot be applied when there are general constraints that span across multiple stages, and that node dominance, in the form of Euler-Lagrange conditions developed in optimal control theory in continuous space, cannot be extended to that in discrete space. This paper is the first to propose efficient dominance relations, in the form of local saddle-point conditions in each stage of a problem, for pruning states that will not lead to locally optimal paths. By utilizing these dominance relations, we develop efficient search algorithms whose complexity, despite exponential, has a much smaller base as compared to that without using the relations. Finally, we demonstrate the performance of our algorithms on some spacecraft planning and scheduling benchmarks and show significant improvements in CPU time and solution quality as compared to those obtained by the existing ASPEN planner.
{"title":"Calculus of variations in discrete space for constrained nonlinear dynamic optimization","authors":"Yixin Chen, B. Wah","doi":"10.1109/TAI.2002.1180789","DOIUrl":"https://doi.org/10.1109/TAI.2002.1180789","url":null,"abstract":"We propose new dominance relations that can speed up significantly the solution process of nonlinear constrained dynamic optimization problems in discrete time and space. We first show that path dominance in dynamic programming cannot be applied when there are general constraints that span across multiple stages, and that node dominance, in the form of Euler-Lagrange conditions developed in optimal control theory in continuous space, cannot be extended to that in discrete space. This paper is the first to propose efficient dominance relations, in the form of local saddle-point conditions in each stage of a problem, for pruning states that will not lead to locally optimal paths. By utilizing these dominance relations, we develop efficient search algorithms whose complexity, despite exponential, has a much smaller base as compared to that without using the relations. Finally, we demonstrate the performance of our algorithms on some spacecraft planning and scheduling benchmarks and show significant improvements in CPU time and solution quality as compared to those obtained by the existing ASPEN planner.","PeriodicalId":197064,"journal":{"name":"14th IEEE International Conference on Tools with Artificial Intelligence, 2002. (ICTAI 2002). Proceedings.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2002-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127834044","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}
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