The Experts below are selected from a list of 255 Experts worldwide ranked by ideXlab platform
Aristides Gionis - One of the best experts on this subject based on the ideXlab platform.
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SIGIR - Query similarity by projecting the query-Flow Graph
Proceeding of the 33rd international ACM SIGIR conference on Research and development in information retrieval - SIGIR '10, 2010Co-Authors: Ilaria Bordino, Debora Donato, Carlos Castillo, Aristides GionisAbstract:Defining a measure of similarity between queries is an interesting and difficult problem. A reliable query-similarity measure can be used in a variety of applications such as query recommendation, query expansion, and advertising. In this paper, we exploit the information present in query logs in order to develop a measure of semantic similarity between queries. Our approach relies on the concept of the query-Flow Graph. The query-Flow Graph aggregates query reformulations from many users: nodes in the Graph represent queries, and two queries are connected if they are likely to appear as part of the same search goal. Our query similarity measure is obtained by projecting the Graph (or appropriate subGraphs of it) on a low-dimensional Euclidean space. Our experiments show that the measure we obtain captures a notion of semantic similarity between queries and it is useful for diversifying query recommendations.
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Query similarity by projecting the query-Flow Graph
2010Co-Authors: Ilaria Bordino, Debora Donato, Carlos Castillo, Aristides GionisAbstract:Defining a measure of similarity between queries is an interesting and difficult problem. A reliable query-similarity measure can be used in a variety of applications such as query recommendation, query expansion, and advertising. In this paper, we exploit the information present in query logs in order to develop a measure of semantic similarity between queries. Our approach relies on the concept of the query-Flow Graph. The query-Flow Graph aggregates query reformulations from many users: nodes in the Graph represent queries, and two queries are connected if they are likely to appear as part of the same search goal. Our query similarity measure is obtained by projecting the Graph (or appropriate subGraphs of it) on a low-dimensional Euclidean space. Our experiments show that the measure we obtain captures a notion of semantic similarity between queries and it is useful for diversifying query recommendations.
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The query-Flow Graph: Model and Applications
Proceeding of the 17th ACM conference on Information and knowledge mining - CIKM '08, 2008Co-Authors: Paolo Boldi, Debora Donato, Aristides Gionis, Carlos Castillo, Francesco Bonchi, Sebastiano VignaAbstract:Query logs record the queries and the actions of the users of search engines, and as such they contain valuable information about the interests, the preferences, and the behavior of the users, as well as their implicit feedback to search engine results. Mining the wealth of information available in the query logs has many important applications including query-log analysis, user profiling and personalization, advertising, query recommendation, and more. In this paper we introduce the query-Flow Graph, a Graph representation of the interesting knowledge about latent querying behavior. Intuitively, in the query-Flow Graph a directed edge from query qi to query qj means that the two queries are likely to be part of the same "search mission". Any path over the query-Flow Graph may be seen as a searching behavior, whose likelihood is given by the strength of the edges along the path. The query-Flow Graph is an outcome of query-log mining and, at the same time, a useful tool for it. We propose a methodology that builds such a Graph by mining time and textual information as well as aggregating queries from different users. Using this approach we build a real-world query-Flow Graph from a large-scale query log and we demonstrate its utility in concrete applications, namely, finding logical sessions, and query recommendation. We believe, however, that the usefulness of the query-Flow Graph goes beyond these two applications.
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CIKM - The query-Flow Graph: model and applications
Proceeding of the 17th ACM conference on Information and knowledge mining - CIKM '08, 2008Co-Authors: Paolo Boldi, Debora Donato, Aristides Gionis, Carlos Castillo, Francesco Bonchi, Sebastiano VignaAbstract:Query logs record the queries and the actions of the users of search engines, and as such they contain valuable information about the interests, the preferences, and the behavior of the users, as well as their implicit feedback to search engine results. Mining the wealth of information available in the query logs has many important applications including query-log analysis, user profiling and personalization, advertising, query recommendation, and more. In this paper we introduce the query-Flow Graph, a Graph representation of the interesting knowledge about latent querying behavior. Intuitively, in the query-Flow Graph a directed edge from query qi to query qj means that the two queries are likely to be part of the same "search mission". Any path over the query-Flow Graph may be seen as a searching behavior, whose likelihood is given by the strength of the edges along the path. The query-Flow Graph is an outcome of query-log mining and, at the same time, a useful tool for it. We propose a methodology that builds such a Graph by mining time and textual information as well as aggregating queries from different users. Using this approach we build a real-world query-Flow Graph from a large-scale query log and we demonstrate its utility in concrete applications, namely, finding logical sessions, and query recommendation. We believe, however, that the usefulness of the query-Flow Graph goes beyond these two applications.
Mummadi Veerachary - One of the best experts on this subject based on the ideXlab platform.
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Signal Flow Graph modeling of two-cell cascade buck converters
Electrical Engineering, 2005Co-Authors: Mummadi VeeracharyAbstract:This paper presents signal Flow Graph nonlinear modeling of two-cell cascade buck converters. A systematic procedure for developing the unified Flow Graph model of the cascade converter is discussed. A simplified procedure is described that can be used to deduce large, small-signal and steady-state models from the unified signal Flow Graph of the converter. Converter performance expressions, and small-signal and steady-state transfer functions are derived. The large-signal model is developed and programmed into a TUTSIM simulator. Converter large-signal responses are obtained against supply and load disturbances. The validity of the proposed signal Flow Graph modeling of cascade converters is verified and comparisons are made via PSIM simulator results. A few experimental results are provided to verify the proposed method.
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signal Flow Graph modelling of multi state boost dc dc converters
IEE Proceedings - Electric Power Applications, 2004Co-Authors: Mummadi VeeracharyAbstract:A systematic procedure and guidelines for developing the unified Flow Graph model of a multi-state boost DC-DC converter is presented. From this unified model it is possible to predetermine the complete behaviour of the converter system. The proposed method provides ease of model formulation and avoids the mathematical complexity involved in obtaining the unified model. Usefulness of the proposed method is demonstrated through an example of three state boost converters. A simplified procedure is described that can be used to deduce large-signal, small-signal and steady-state from the unified signal Flow Graph of the converter. Large-signal models models have been developed and programmed in the TUTSIM simulator. Large-signal responses against supply and load disturbances were obtained. Theoretical results, obtained from the proposed signal Flow Graph method, are compared with PSIM power electronic simulator results. Experimental results are provided to validate the proposed modelling method.
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general rules for signal Flow Graph modeling and analysis of dc dc converters
IEEE Transactions on Aerospace and Electronic Systems, 2004Co-Authors: Mummadi VeeracharyAbstract:Signal Flow Graph (SFG) nonlinear modeling approach is well known for modeling dc-dc converters. However, all possible SFGs of a given dc-dc converter system will not yield the generalized Graph. A systematic procedure and guidelines for developing unified Flow Graph models of the dc-dc boost converters, from which complete behavior can be determined is presented. Usefulness of the proposed method is demonstrated through examples. As an illustration a 2-cell cascade boost and interleaved boost converter systems are taken as examples. Derivation of large, small-signal and steady-state models from generalized Flow Graph is also demonstrated. Large-signal model is developed and programmed in TUTSIM simulator. Large-signal, responses against supply and load disturbances are obtained. Experimental observations are provided to validate the proposed algorithm.
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Signal Flow Graph modelling of interleaved buck converters
International Journal of Circuit Theory and Applications, 2003Co-Authors: Mummadi Veerachary, Tomonobu Senjyu, Katsumi UezatoAbstract:This paper presents a systematic development of unified signal Flow Graph model for an interleaved buck converter system operating in continuous inductor current mode. From this signal Flow Graph small, large-signal and steady-state models are developed, which are useful to study the converter dynamic and steady-state behaviour. Converter performance expressions like steady-state voltage gain, efficiency expressions and other small-signal characteristic transfer functions are derived. Development of unified signal Flow Graph is explained for a 3-cell interleaved converter system. Derivation of large, small-signal and steady-state models from the unified signal Flow Graph is demonstrated by considering a 2-cell interleaved buck converter system. Large signal model was programmed in TUTSIM simulator, and the large-signal responses against supply, load disturbances were predicted. Signal Flow Graph analysis results are validated with PSIM simulations. Further, the mathematical models obtained from the signal Flow Graph modelling are in agreement with those obtained from the state-space averaging technique. Copyright © 2003 John Wiley & Sons, Ltd.
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Analysis of interleaved dual boost converter with integrated magnetics: signal Flow Graph approach
IEE Proceedings - Electric Power Applications, 2003Co-Authors: Mummadi VeeracharyAbstract:A systematic development of a unified signal Flow Graph model for an interleaved boost converter with coupled inductor system operating in continuous current mode is presented. This signal Flow Graph approach provides a means to directly translate the switching converter to its Graphic model, from which steady-state and dynamic behaviour of the converter can easily be studied. Development of a unified signal Flow Graph is explained and derivation of large, small-signal and steady-state models is demonstrated. Converter performance expressions such as steady-state voltage gain, efficiency expressions and small-signal characteristic transfer functions are also derived. Large-signal responses against supply and load disturbances are predicted by programming the corresponding signal Flow Graph in the TUTSIM simulator. Experimental observations are provided to validate the signal Flow Graph modelling method. Frequency response characteristics generated from the signal Flow method are validated with PSpice simulations. Further, the mathematical models obtained from the signal Flow Graph modelling are in agreement with those obtained from the state-space averaging technique.
Slobodan Cuk - One of the best experts on this subject based on the ideXlab platform.
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Switching Flow-Graph nonlinear modeling technique
IEEE Transactions on Power Electronics, 1994Co-Authors: Keyue Smedley, Slobodan CukAbstract:A unified Graphical modeling technique, “Switching Flow-Graph” is developed to study the nonlinear dynamic behavior of pulse-width-modulated (PWM) switching converters. Switching converters are variable structure systems with linear subsystems. Each subsystem can be represented by a Flow-Graph. The Switching Flow-Graph is obtained by combining the FlowGraphs of the subsystems through the use of switching branches. The Switching Flow-Graph model is easy to derive, and it provides a visual representation of a switching converter system. Experiments demonstrate that the Switching Flow-Graph model has very good accuracy
Debora Donato - One of the best experts on this subject based on the ideXlab platform.
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SIGIR - Query similarity by projecting the query-Flow Graph
Proceeding of the 33rd international ACM SIGIR conference on Research and development in information retrieval - SIGIR '10, 2010Co-Authors: Ilaria Bordino, Debora Donato, Carlos Castillo, Aristides GionisAbstract:Defining a measure of similarity between queries is an interesting and difficult problem. A reliable query-similarity measure can be used in a variety of applications such as query recommendation, query expansion, and advertising. In this paper, we exploit the information present in query logs in order to develop a measure of semantic similarity between queries. Our approach relies on the concept of the query-Flow Graph. The query-Flow Graph aggregates query reformulations from many users: nodes in the Graph represent queries, and two queries are connected if they are likely to appear as part of the same search goal. Our query similarity measure is obtained by projecting the Graph (or appropriate subGraphs of it) on a low-dimensional Euclidean space. Our experiments show that the measure we obtain captures a notion of semantic similarity between queries and it is useful for diversifying query recommendations.
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Query similarity by projecting the query-Flow Graph
2010Co-Authors: Ilaria Bordino, Debora Donato, Carlos Castillo, Aristides GionisAbstract:Defining a measure of similarity between queries is an interesting and difficult problem. A reliable query-similarity measure can be used in a variety of applications such as query recommendation, query expansion, and advertising. In this paper, we exploit the information present in query logs in order to develop a measure of semantic similarity between queries. Our approach relies on the concept of the query-Flow Graph. The query-Flow Graph aggregates query reformulations from many users: nodes in the Graph represent queries, and two queries are connected if they are likely to appear as part of the same search goal. Our query similarity measure is obtained by projecting the Graph (or appropriate subGraphs of it) on a low-dimensional Euclidean space. Our experiments show that the measure we obtain captures a notion of semantic similarity between queries and it is useful for diversifying query recommendations.
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The query-Flow Graph: Model and Applications
Proceeding of the 17th ACM conference on Information and knowledge mining - CIKM '08, 2008Co-Authors: Paolo Boldi, Debora Donato, Aristides Gionis, Carlos Castillo, Francesco Bonchi, Sebastiano VignaAbstract:Query logs record the queries and the actions of the users of search engines, and as such they contain valuable information about the interests, the preferences, and the behavior of the users, as well as their implicit feedback to search engine results. Mining the wealth of information available in the query logs has many important applications including query-log analysis, user profiling and personalization, advertising, query recommendation, and more. In this paper we introduce the query-Flow Graph, a Graph representation of the interesting knowledge about latent querying behavior. Intuitively, in the query-Flow Graph a directed edge from query qi to query qj means that the two queries are likely to be part of the same "search mission". Any path over the query-Flow Graph may be seen as a searching behavior, whose likelihood is given by the strength of the edges along the path. The query-Flow Graph is an outcome of query-log mining and, at the same time, a useful tool for it. We propose a methodology that builds such a Graph by mining time and textual information as well as aggregating queries from different users. Using this approach we build a real-world query-Flow Graph from a large-scale query log and we demonstrate its utility in concrete applications, namely, finding logical sessions, and query recommendation. We believe, however, that the usefulness of the query-Flow Graph goes beyond these two applications.
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CIKM - The query-Flow Graph: model and applications
Proceeding of the 17th ACM conference on Information and knowledge mining - CIKM '08, 2008Co-Authors: Paolo Boldi, Debora Donato, Aristides Gionis, Carlos Castillo, Francesco Bonchi, Sebastiano VignaAbstract:Query logs record the queries and the actions of the users of search engines, and as such they contain valuable information about the interests, the preferences, and the behavior of the users, as well as their implicit feedback to search engine results. Mining the wealth of information available in the query logs has many important applications including query-log analysis, user profiling and personalization, advertising, query recommendation, and more. In this paper we introduce the query-Flow Graph, a Graph representation of the interesting knowledge about latent querying behavior. Intuitively, in the query-Flow Graph a directed edge from query qi to query qj means that the two queries are likely to be part of the same "search mission". Any path over the query-Flow Graph may be seen as a searching behavior, whose likelihood is given by the strength of the edges along the path. The query-Flow Graph is an outcome of query-log mining and, at the same time, a useful tool for it. We propose a methodology that builds such a Graph by mining time and textual information as well as aggregating queries from different users. Using this approach we build a real-world query-Flow Graph from a large-scale query log and we demonstrate its utility in concrete applications, namely, finding logical sessions, and query recommendation. We believe, however, that the usefulness of the query-Flow Graph goes beyond these two applications.
Carlos Castillo - One of the best experts on this subject based on the ideXlab platform.
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SIGIR - Query similarity by projecting the query-Flow Graph
Proceeding of the 33rd international ACM SIGIR conference on Research and development in information retrieval - SIGIR '10, 2010Co-Authors: Ilaria Bordino, Debora Donato, Carlos Castillo, Aristides GionisAbstract:Defining a measure of similarity between queries is an interesting and difficult problem. A reliable query-similarity measure can be used in a variety of applications such as query recommendation, query expansion, and advertising. In this paper, we exploit the information present in query logs in order to develop a measure of semantic similarity between queries. Our approach relies on the concept of the query-Flow Graph. The query-Flow Graph aggregates query reformulations from many users: nodes in the Graph represent queries, and two queries are connected if they are likely to appear as part of the same search goal. Our query similarity measure is obtained by projecting the Graph (or appropriate subGraphs of it) on a low-dimensional Euclidean space. Our experiments show that the measure we obtain captures a notion of semantic similarity between queries and it is useful for diversifying query recommendations.
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Query similarity by projecting the query-Flow Graph
2010Co-Authors: Ilaria Bordino, Debora Donato, Carlos Castillo, Aristides GionisAbstract:Defining a measure of similarity between queries is an interesting and difficult problem. A reliable query-similarity measure can be used in a variety of applications such as query recommendation, query expansion, and advertising. In this paper, we exploit the information present in query logs in order to develop a measure of semantic similarity between queries. Our approach relies on the concept of the query-Flow Graph. The query-Flow Graph aggregates query reformulations from many users: nodes in the Graph represent queries, and two queries are connected if they are likely to appear as part of the same search goal. Our query similarity measure is obtained by projecting the Graph (or appropriate subGraphs of it) on a low-dimensional Euclidean space. Our experiments show that the measure we obtain captures a notion of semantic similarity between queries and it is useful for diversifying query recommendations.
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The query-Flow Graph: Model and Applications
Proceeding of the 17th ACM conference on Information and knowledge mining - CIKM '08, 2008Co-Authors: Paolo Boldi, Debora Donato, Aristides Gionis, Carlos Castillo, Francesco Bonchi, Sebastiano VignaAbstract:Query logs record the queries and the actions of the users of search engines, and as such they contain valuable information about the interests, the preferences, and the behavior of the users, as well as their implicit feedback to search engine results. Mining the wealth of information available in the query logs has many important applications including query-log analysis, user profiling and personalization, advertising, query recommendation, and more. In this paper we introduce the query-Flow Graph, a Graph representation of the interesting knowledge about latent querying behavior. Intuitively, in the query-Flow Graph a directed edge from query qi to query qj means that the two queries are likely to be part of the same "search mission". Any path over the query-Flow Graph may be seen as a searching behavior, whose likelihood is given by the strength of the edges along the path. The query-Flow Graph is an outcome of query-log mining and, at the same time, a useful tool for it. We propose a methodology that builds such a Graph by mining time and textual information as well as aggregating queries from different users. Using this approach we build a real-world query-Flow Graph from a large-scale query log and we demonstrate its utility in concrete applications, namely, finding logical sessions, and query recommendation. We believe, however, that the usefulness of the query-Flow Graph goes beyond these two applications.
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CIKM - The query-Flow Graph: model and applications
Proceeding of the 17th ACM conference on Information and knowledge mining - CIKM '08, 2008Co-Authors: Paolo Boldi, Debora Donato, Aristides Gionis, Carlos Castillo, Francesco Bonchi, Sebastiano VignaAbstract:Query logs record the queries and the actions of the users of search engines, and as such they contain valuable information about the interests, the preferences, and the behavior of the users, as well as their implicit feedback to search engine results. Mining the wealth of information available in the query logs has many important applications including query-log analysis, user profiling and personalization, advertising, query recommendation, and more. In this paper we introduce the query-Flow Graph, a Graph representation of the interesting knowledge about latent querying behavior. Intuitively, in the query-Flow Graph a directed edge from query qi to query qj means that the two queries are likely to be part of the same "search mission". Any path over the query-Flow Graph may be seen as a searching behavior, whose likelihood is given by the strength of the edges along the path. The query-Flow Graph is an outcome of query-log mining and, at the same time, a useful tool for it. We propose a methodology that builds such a Graph by mining time and textual information as well as aggregating queries from different users. Using this approach we build a real-world query-Flow Graph from a large-scale query log and we demonstrate its utility in concrete applications, namely, finding logical sessions, and query recommendation. We believe, however, that the usefulness of the query-Flow Graph goes beyond these two applications.
