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Autor Zhang, Wei Emma |
Documentos disponibles escritos por este autor (3)
Crear una solicitud de compra Refinar búsquedaAdvanced Data Mining and Applications / Cong, Gao ; Peng, Wen-Chih ; Zhang, Wei Emma ; Li, Chengliang ; Sun, Aixin
TÃtulo : Advanced Data Mining and Applications : 13th International Conference, ADMA 2017, Singapore, November 5–6, 2017, Proceedings / Tipo de documento: documento electrónico Autores: Cong, Gao, ; Peng, Wen-Chih, ; Zhang, Wei Emma, ; Li, Chengliang, ; Sun, Aixin, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2017 Número de páginas: XVII, 881 p. 264 ilustraciones ISBN/ISSN/DL: 978-3-319-69179-4 Nota general: Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Idioma : Inglés (eng) Palabras clave: Inteligencia artificial Procesamiento de datos Sistemas de almacenamiento y recuperación de información. Software de la aplicacion Procesamiento del lenguaje natural (Informática) Red de computadoras MinerÃa de datos y descubrimiento de conocimientos Almacenamiento y recuperación de información Aplicaciones informáticas y de sistemas de información Procesamiento del lenguaje natural (PNL) Redes de comunicación informática Clasificación: 006.3 Resumen: Este libro constituye las actas arbitradas de la 13.ª Conferencia Internacional sobre Aplicaciones y MinerÃa de Datos Avanzados, ADMA 2017, celebrada en Singapur en noviembre de 2017. Los 20 artÃculos completos y 38 breves presentados en este volumen fueron cuidadosamente revisados ​​y seleccionados entre 118 presentaciones. Los artÃculos se organizaron en secciones temáticas denominadas: base de datos y aprendizaje automático distribuido; sistema de recomendación; red social y medios sociales; aprendizaje automático; métodos de clasificación y agrupamiento; modelado de comportamiento y elaboración de perfiles de usuarios; bioinformática y análisis de datos médicos; datos espacio-temporales; procesamiento de lenguaje natural y minerÃa de textos; aplicaciones de minerÃa de datos; aplicaciones; y demostraciones. . Nota de contenido: Database and Distributed Machine Learning -- Querying and Mining Strings Made Easy Distributed Training Large-Scale Deep Architectures -- Fault Detection and Localization in Distributed Systems using Recurrent Convolutional Neural Networks -- Discovering Group Skylines with Constraints by Early Candidate Pruning -- Comparing MapReduce-Based k-NN Similarity Joins On Hadoop For High-dimensional Data -- A Higher-Fidelity Frugal Quantile Estimator -- Recommender System -- Fair Recommendations Through Diversity Promotion -- A Hierarchical Bayesian Factorization Model for Implicit and Explicit Feedback Data -- Empirical Analysis of Factors Influencing Twitter Hashtag Recommendation on Detected Communities -- Group Recommender Model Based on Preference Interaction -- Identification of Grey Sheep Users By Histogram Intersection In Recommender Systems -- Social Network and Social Media -- A Feature-based Approach for the Redefined Link Prediction Problem in Signed Networks -- From Mutual Friends to Overlapping Community Detection: A Non-negative Matrix Factorization Approach -- Calling for Response: Automatically Distinguishing Situation-aware Tweets During Crises -- Efficient Revenue Maximization for Viral Marketing in Social Networks -- Generating Life Course Trajectory Sequences with Recurrent Neural Networks and Application to Early Detection on Social Disadvantage -- FRISK: A Multilingual Approach to Find twitteR InterestS via wiKipedia -- A Solution to Tweet-Based User Identification across Online Social Networks -- Machine Learning -- Supervised Feature Selection Algorithm Based on Low-Rank and Manifold Learning -- Mixed Membership Sparse Gaussian Conditional Random Fields -- Effects of Dynamic Subspacing in Random Forest -- Diversity and Locality in Multi-Component, Multi-Layer Predictive Systems: A Mutual Information Based Approach -- Hybrid Subspace Mixture Models For Prediction and Anomaly Detection in High Dimensions -- Classification and Clustering Methods --  StruClus: Scalable Structural Graph Set Clustering with Representative Sampling -- Employing Hierarchical Clustering and Reinforcement Learning for Attribute-based Zero-Shot Classification -- Environmental Sound Recognition using Masked Conditional Neural Networks -- Analyzing Performance of Classification Techniques in Detecting Epileptic Seizure -- A Framework for Clustering and Dynamic Maintenance of XML Documents -- Language-independent Twitter Classification using Character-based Convolutional Networks -- Behavior Modeling and User Profiling -- Modeling Check-in Behavior with Geographical Neighborhood Influence of Venues -- An empirical study on collective online behaviors of extremist supporters. -Your Moves, Your Device: Establishing Behavior Profiles using Tensors -- An Approach for Identifying Author Profiles of Blogs -- Generating Topics of Interests for Research Communities -- An Evolutionary Approach for Learning Conditional Preference Network from Inconsistent Examples -- Bioinformatic and Medical Data Analysis -- Predicting Clinical Outcomes of Alzheimer's Disease from Complex Brain Networks -- Doctoral Advisor or Medical Condition: Towards Entity-specific Rankings of Knowledge Base Properties -- Multiclass Lung Cancer Diagnosis by Gene Expression Programming and Microarray Datasets -- Drug-drug Interaction Extraction via Recurrent Neural Network with Multiple Attention Layers -- Spatio-temporal Data -- People-Centric Mobile Crowdsensing Platform for Urban Design -- Long-Term User Location Prediction Using Deep Learning and Periodic Pattern Mining -- An Intelligent Weighted Fuzzy Time Series Model Based on A Sine-Cosine Adaptive Human Learning Optimization Algorithm and Its Application to Financial Markets Forecasting -- Mobile Robot Scheduling with Multiple Trips and Time Windows -- Natural Language Processing and Text Mining -- Feature Analysis for Duplicate Detection in Programming QA Communities -- A Joint Human/Machine Process for Coding Events and Conflict Drivers -- Quality Prediction of Newly Proposed Questions in CQA by Leveraging Weakly Supervised Learning -- Improving Chinese Sentiment Analysis via Segmentation-based Representation Using Parallel CNN -- Entity Recognition by Distant Supervision with Soft List Constraint -- Structured Sentiment Analysis -- Data Mining Applications -- Improving Real-Time Bidding Using a Constrained Markov Decision Process -- PowerLSTM: Power Demand Forecasting Using Long Short-Term Memory Neural Network -- Identifying Unreliable Sensors Without a Knowledge of the Ground Truth in Deceptive Environments -- Color-sketch simulator: a guide for color-based visual known-item search -- Applications -- Making Use of External Company Data to Improve the Classification of Bank Transactions -- Mining Load Profile Patterns for Australian Electricity Consumers -- STA: a Spatio-temporal Thematic Analytics Framework for Urban Ground Sensing -- Privacy and Utility Preservation for Location Data Using Stay Region Analysis -- Location-aware Human Activity Recognition -- Demos -- SWYSWYK: a new Sharing Paradigm for the Personal Cloud -- Tools and Infrastructure for Supporting Enterprise Knowledge Graphs -- An Interactive Web-based Toolset for Knowledge Discovery from Short Text Log Data -- Carbon: Forecasting Civil Unrest Events by Monitoring News and Social Media -- A system for Querying and Analyzing Urban Regions -- Detect tracking behavior among trajectory data. Tipo de medio : Computadora Summary : This book constitutes the refereed proceedings of the 13th International Conference on Advanced Data Mining and Applications, ADMA 2017, held in Singapore in November 2017. The 20 full and 38 short papers presented in this volume were carefully reviewed and selected from 118 submissions. The papers were organized in topical sections named: database and distributed machine learning; recommender system; social network and social media; machine learning; classification and clustering methods; behavior modeling and user profiling; bioinformatics and medical data analysis; spatio-temporal data; natural language processing and text mining; data mining applications; applications; and demos. . Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Advanced Data Mining and Applications : 13th International Conference, ADMA 2017, Singapore, November 5–6, 2017, Proceedings / [documento electrónico] / Cong, Gao, ; Peng, Wen-Chih, ; Zhang, Wei Emma, ; Li, Chengliang, ; Sun, Aixin, . - 1 ed. . - [s.l.] : Springer, 2017 . - XVII, 881 p. 264 ilustraciones.
ISBN : 978-3-319-69179-4
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
Idioma : Inglés (eng)
Palabras clave: Inteligencia artificial Procesamiento de datos Sistemas de almacenamiento y recuperación de información. Software de la aplicacion Procesamiento del lenguaje natural (Informática) Red de computadoras MinerÃa de datos y descubrimiento de conocimientos Almacenamiento y recuperación de información Aplicaciones informáticas y de sistemas de información Procesamiento del lenguaje natural (PNL) Redes de comunicación informática Clasificación: 006.3 Resumen: Este libro constituye las actas arbitradas de la 13.ª Conferencia Internacional sobre Aplicaciones y MinerÃa de Datos Avanzados, ADMA 2017, celebrada en Singapur en noviembre de 2017. Los 20 artÃculos completos y 38 breves presentados en este volumen fueron cuidadosamente revisados ​​y seleccionados entre 118 presentaciones. Los artÃculos se organizaron en secciones temáticas denominadas: base de datos y aprendizaje automático distribuido; sistema de recomendación; red social y medios sociales; aprendizaje automático; métodos de clasificación y agrupamiento; modelado de comportamiento y elaboración de perfiles de usuarios; bioinformática y análisis de datos médicos; datos espacio-temporales; procesamiento de lenguaje natural y minerÃa de textos; aplicaciones de minerÃa de datos; aplicaciones; y demostraciones. . Nota de contenido: Database and Distributed Machine Learning -- Querying and Mining Strings Made Easy Distributed Training Large-Scale Deep Architectures -- Fault Detection and Localization in Distributed Systems using Recurrent Convolutional Neural Networks -- Discovering Group Skylines with Constraints by Early Candidate Pruning -- Comparing MapReduce-Based k-NN Similarity Joins On Hadoop For High-dimensional Data -- A Higher-Fidelity Frugal Quantile Estimator -- Recommender System -- Fair Recommendations Through Diversity Promotion -- A Hierarchical Bayesian Factorization Model for Implicit and Explicit Feedback Data -- Empirical Analysis of Factors Influencing Twitter Hashtag Recommendation on Detected Communities -- Group Recommender Model Based on Preference Interaction -- Identification of Grey Sheep Users By Histogram Intersection In Recommender Systems -- Social Network and Social Media -- A Feature-based Approach for the Redefined Link Prediction Problem in Signed Networks -- From Mutual Friends to Overlapping Community Detection: A Non-negative Matrix Factorization Approach -- Calling for Response: Automatically Distinguishing Situation-aware Tweets During Crises -- Efficient Revenue Maximization for Viral Marketing in Social Networks -- Generating Life Course Trajectory Sequences with Recurrent Neural Networks and Application to Early Detection on Social Disadvantage -- FRISK: A Multilingual Approach to Find twitteR InterestS via wiKipedia -- A Solution to Tweet-Based User Identification across Online Social Networks -- Machine Learning -- Supervised Feature Selection Algorithm Based on Low-Rank and Manifold Learning -- Mixed Membership Sparse Gaussian Conditional Random Fields -- Effects of Dynamic Subspacing in Random Forest -- Diversity and Locality in Multi-Component, Multi-Layer Predictive Systems: A Mutual Information Based Approach -- Hybrid Subspace Mixture Models For Prediction and Anomaly Detection in High Dimensions -- Classification and Clustering Methods --  StruClus: Scalable Structural Graph Set Clustering with Representative Sampling -- Employing Hierarchical Clustering and Reinforcement Learning for Attribute-based Zero-Shot Classification -- Environmental Sound Recognition using Masked Conditional Neural Networks -- Analyzing Performance of Classification Techniques in Detecting Epileptic Seizure -- A Framework for Clustering and Dynamic Maintenance of XML Documents -- Language-independent Twitter Classification using Character-based Convolutional Networks -- Behavior Modeling and User Profiling -- Modeling Check-in Behavior with Geographical Neighborhood Influence of Venues -- An empirical study on collective online behaviors of extremist supporters. -Your Moves, Your Device: Establishing Behavior Profiles using Tensors -- An Approach for Identifying Author Profiles of Blogs -- Generating Topics of Interests for Research Communities -- An Evolutionary Approach for Learning Conditional Preference Network from Inconsistent Examples -- Bioinformatic and Medical Data Analysis -- Predicting Clinical Outcomes of Alzheimer's Disease from Complex Brain Networks -- Doctoral Advisor or Medical Condition: Towards Entity-specific Rankings of Knowledge Base Properties -- Multiclass Lung Cancer Diagnosis by Gene Expression Programming and Microarray Datasets -- Drug-drug Interaction Extraction via Recurrent Neural Network with Multiple Attention Layers -- Spatio-temporal Data -- People-Centric Mobile Crowdsensing Platform for Urban Design -- Long-Term User Location Prediction Using Deep Learning and Periodic Pattern Mining -- An Intelligent Weighted Fuzzy Time Series Model Based on A Sine-Cosine Adaptive Human Learning Optimization Algorithm and Its Application to Financial Markets Forecasting -- Mobile Robot Scheduling with Multiple Trips and Time Windows -- Natural Language Processing and Text Mining -- Feature Analysis for Duplicate Detection in Programming QA Communities -- A Joint Human/Machine Process for Coding Events and Conflict Drivers -- Quality Prediction of Newly Proposed Questions in CQA by Leveraging Weakly Supervised Learning -- Improving Chinese Sentiment Analysis via Segmentation-based Representation Using Parallel CNN -- Entity Recognition by Distant Supervision with Soft List Constraint -- Structured Sentiment Analysis -- Data Mining Applications -- Improving Real-Time Bidding Using a Constrained Markov Decision Process -- PowerLSTM: Power Demand Forecasting Using Long Short-Term Memory Neural Network -- Identifying Unreliable Sensors Without a Knowledge of the Ground Truth in Deceptive Environments -- Color-sketch simulator: a guide for color-based visual known-item search -- Applications -- Making Use of External Company Data to Improve the Classification of Bank Transactions -- Mining Load Profile Patterns for Australian Electricity Consumers -- STA: a Spatio-temporal Thematic Analytics Framework for Urban Ground Sensing -- Privacy and Utility Preservation for Location Data Using Stay Region Analysis -- Location-aware Human Activity Recognition -- Demos -- SWYSWYK: a new Sharing Paradigm for the Personal Cloud -- Tools and Infrastructure for Supporting Enterprise Knowledge Graphs -- An Interactive Web-based Toolset for Knowledge Discovery from Short Text Log Data -- Carbon: Forecasting Civil Unrest Events by Monitoring News and Social Media -- A system for Querying and Analyzing Urban Regions -- Detect tracking behavior among trajectory data. Tipo de medio : Computadora Summary : This book constitutes the refereed proceedings of the 13th International Conference on Advanced Data Mining and Applications, ADMA 2017, held in Singapore in November 2017. The 20 full and 38 short papers presented in this volume were carefully reviewed and selected from 118 submissions. The papers were organized in topical sections named: database and distributed machine learning; recommender system; social network and social media; machine learning; classification and clustering methods; behavior modeling and user profiling; bioinformatics and medical data analysis; spatio-temporal data; natural language processing and text mining; data mining applications; applications; and demos. . Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
TÃtulo : Managing Data From Knowledge Bases: Querying and Extraction Tipo de documento: documento electrónico Autores: Zhang, Wei Emma, ; Sheng, Quan Z., Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2018 Número de páginas: XIII, 139 p. 41 ilustraciones, 32 ilustraciones en color. ISBN/ISSN/DL: 978-3-319-94935-2 Nota general: Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Idioma : Inglés (eng) Palabras clave: Procesamiento de datos Sistemas de almacenamiento y recuperación de información. Software de la aplicacion MinerÃa de datos y descubrimiento de conocimientos Almacenamiento y recuperación de información Aplicaciones informáticas y de sistemas de información Clasificación: 6.312 Resumen: En este libro, los autores abordan primero los temas de investigación proporcionando un escenario motivador, seguido de la exploración de los principios y técnicas de los temas desafiantes. Luego resuelven las cuestiones de investigación planteadas desarrollando una serie de metodologÃas. Más especÃficamente, los autores estudian la optimización de consultas y abordan la predicción del rendimiento de las consultas para la recuperación de conocimientos. También manejan el procesamiento de datos no estructurados y la agrupación de datos para la extracción de conocimientos. Para optimizar las consultas emitidas a través de interfaces frente a bases de conocimiento, los autores proponen una capa de optimización basada en caché entre los consumidores y la interfaz de consulta para facilitar la consulta y resolver el problema de latencia. El caché depende de un método de aprendizaje novedoso que considera los patrones de consulta de las consultas históricas de los individuos sin tener conocimiento de los sistemas de respaldo de la base de conocimientos. Para predecir el rendimiento de las consultas para una programación de consultas adecuada, los autores examinan las caracterÃsticas estructurales y sintácticas de las consultas y aplican múltiples modelos de predicción ampliamente adoptados. Su enfoque de modelado de caracterÃsticas evita el requisito de conocimiento tanto en los lenguajes de consulta como en el sistema. Para extraer conocimiento de fuentes web no estructuradas, los autores examinan dos tipos de fuentes web que contienen datos no estructurados: el código fuente de los repositorios web y las publicaciones en comunidades de programación de preguntas y respuestas. Utilizan técnicas de procesamiento del lenguaje natural para preprocesar los códigos fuente y obtener los elementos del lenguaje natural. Luego aplican técnicas tradicionales de extracción de conocimientos para extraer conocimientos. Para los datos de las comunidades de programación de preguntas y respuestas, los autores intentan construir una base de conocimientos de programación comenzando con parafraseando problemas de identificación y desarrollando caracterÃsticas novedosas para identificar con precisión publicaciones duplicadas. Para la extracción de conocimiento de un dominio especÃfico, los autores proponen utilizar una técnica de agrupamiento para separar el conocimiento en diferentes grupos. Se centran en desarrollar un nuevo algoritmo de agrupación que utilice múltiples restricciones en la tarea de optimización y logre un rendimiento rápido y preciso. Para cada modelo y enfoque presentado en esta disertación, los autores han realizado extensos experimentos para evaluarlo utilizando conjuntos de datos públicos o datos sintéticos que generaron. Nota de contenido: 1 Introduction -- 2 Cache Based Optimization for Querying Curated Knowledge Bases -- 3 Query Performance Prediction on Knowledge Base -- 4 An Efficient Knowledge Clustering Algorithm -- 5 Knowledge Extraction from Unstructured Data on the Web -- 6 Building Knowledge Bases from Unstructured Data on the Web -- 7 Conclusion. Tipo de medio : Computadora Summary : In this book, the authors first address the research issues by providing a motivating scenario, followed by the exploration of the principles and techniques of the challenging topics. Then they solve the raised research issues by developing a series of methodologies. More specifically, the authors study the query optimization and tackle the query performance prediction for knowledge retrieval. They also handle unstructured data processing, data clustering for knowledge extraction. To optimize the queries issued through interfaces against knowledge bases, the authors propose a cache-based optimization layer between consumers and the querying interface to facilitate the querying and solve the latency issue. The cache depends on a novel learning method that considers the querying patterns from individual's historical queries without having knowledge of the backing systems of the knowledge base. To predict the query performance for appropriate query scheduling, the authors examine the queries' structural and syntactical features and apply multiple widely adopted prediction models. Their feature modelling approach eschews the knowledge requirement on both the querying languages and system. To extract knowledge from unstructured Web sources, the authors examine two kinds of Web sources containing unstructured data: the source code from Web repositories and the posts in programming question-answering communities. They use natural language processing techniques to pre-process the source codes and obtain the natural language elements. Then they apply traditional knowledge extraction techniques to extract knowledge. For the data from programming question-answering communities, the authors make the attempt towards building programming knowledge base by starting with paraphrase identification problems and develop novel features to accurately identify duplicate posts. For domain specific knowledge extraction, the authors propose to use a clustering technique toseparate knowledge into different groups. They focus on developing a new clustering algorithm that uses manifold constraints in the optimization task and achieves fast and accurate performance. For each model and approach presented in this dissertation, the authors have conducted extensive experiments to evaluate it using either public dataset or synthetic data they generated. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Managing Data From Knowledge Bases: Querying and Extraction [documento electrónico] / Zhang, Wei Emma, ; Sheng, Quan Z., . - 1 ed. . - [s.l.] : Springer, 2018 . - XIII, 139 p. 41 ilustraciones, 32 ilustraciones en color.
ISBN : 978-3-319-94935-2
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
Idioma : Inglés (eng)
Palabras clave: Procesamiento de datos Sistemas de almacenamiento y recuperación de información. Software de la aplicacion MinerÃa de datos y descubrimiento de conocimientos Almacenamiento y recuperación de información Aplicaciones informáticas y de sistemas de información Clasificación: 6.312 Resumen: En este libro, los autores abordan primero los temas de investigación proporcionando un escenario motivador, seguido de la exploración de los principios y técnicas de los temas desafiantes. Luego resuelven las cuestiones de investigación planteadas desarrollando una serie de metodologÃas. Más especÃficamente, los autores estudian la optimización de consultas y abordan la predicción del rendimiento de las consultas para la recuperación de conocimientos. También manejan el procesamiento de datos no estructurados y la agrupación de datos para la extracción de conocimientos. Para optimizar las consultas emitidas a través de interfaces frente a bases de conocimiento, los autores proponen una capa de optimización basada en caché entre los consumidores y la interfaz de consulta para facilitar la consulta y resolver el problema de latencia. El caché depende de un método de aprendizaje novedoso que considera los patrones de consulta de las consultas históricas de los individuos sin tener conocimiento de los sistemas de respaldo de la base de conocimientos. Para predecir el rendimiento de las consultas para una programación de consultas adecuada, los autores examinan las caracterÃsticas estructurales y sintácticas de las consultas y aplican múltiples modelos de predicción ampliamente adoptados. Su enfoque de modelado de caracterÃsticas evita el requisito de conocimiento tanto en los lenguajes de consulta como en el sistema. Para extraer conocimiento de fuentes web no estructuradas, los autores examinan dos tipos de fuentes web que contienen datos no estructurados: el código fuente de los repositorios web y las publicaciones en comunidades de programación de preguntas y respuestas. Utilizan técnicas de procesamiento del lenguaje natural para preprocesar los códigos fuente y obtener los elementos del lenguaje natural. Luego aplican técnicas tradicionales de extracción de conocimientos para extraer conocimientos. Para los datos de las comunidades de programación de preguntas y respuestas, los autores intentan construir una base de conocimientos de programación comenzando con parafraseando problemas de identificación y desarrollando caracterÃsticas novedosas para identificar con precisión publicaciones duplicadas. Para la extracción de conocimiento de un dominio especÃfico, los autores proponen utilizar una técnica de agrupamiento para separar el conocimiento en diferentes grupos. Se centran en desarrollar un nuevo algoritmo de agrupación que utilice múltiples restricciones en la tarea de optimización y logre un rendimiento rápido y preciso. Para cada modelo y enfoque presentado en esta disertación, los autores han realizado extensos experimentos para evaluarlo utilizando conjuntos de datos públicos o datos sintéticos que generaron. Nota de contenido: 1 Introduction -- 2 Cache Based Optimization for Querying Curated Knowledge Bases -- 3 Query Performance Prediction on Knowledge Base -- 4 An Efficient Knowledge Clustering Algorithm -- 5 Knowledge Extraction from Unstructured Data on the Web -- 6 Building Knowledge Bases from Unstructured Data on the Web -- 7 Conclusion. Tipo de medio : Computadora Summary : In this book, the authors first address the research issues by providing a motivating scenario, followed by the exploration of the principles and techniques of the challenging topics. Then they solve the raised research issues by developing a series of methodologies. More specifically, the authors study the query optimization and tackle the query performance prediction for knowledge retrieval. They also handle unstructured data processing, data clustering for knowledge extraction. To optimize the queries issued through interfaces against knowledge bases, the authors propose a cache-based optimization layer between consumers and the querying interface to facilitate the querying and solve the latency issue. The cache depends on a novel learning method that considers the querying patterns from individual's historical queries without having knowledge of the backing systems of the knowledge base. To predict the query performance for appropriate query scheduling, the authors examine the queries' structural and syntactical features and apply multiple widely adopted prediction models. Their feature modelling approach eschews the knowledge requirement on both the querying languages and system. To extract knowledge from unstructured Web sources, the authors examine two kinds of Web sources containing unstructured data: the source code from Web repositories and the posts in programming question-answering communities. They use natural language processing techniques to pre-process the source codes and obtain the natural language elements. Then they apply traditional knowledge extraction techniques to extract knowledge. For the data from programming question-answering communities, the authors make the attempt towards building programming knowledge base by starting with paraphrase identification problems and develop novel features to accurately identify duplicate posts. For domain specific knowledge extraction, the authors propose to use a clustering technique toseparate knowledge into different groups. They focus on developing a new clustering algorithm that uses manifold constraints in the optimization task and achieves fast and accurate performance. For each model and approach presented in this dissertation, the authors have conducted extensive experiments to evaluate it using either public dataset or synthetic data they generated. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
TÃtulo : Service Research and Innovation : 5th and 6th Australasian Symposium, ASSRI 2015 and ASSRI 2017, Sydney, NSW, Australia, November 2–3, 2015, and October 19–20, 2017, Revised Selected Papers / Tipo de documento: documento electrónico Autores: Beheshti, Amin, ; Hashmi, Mustafa, ; Dong, Hai, ; Zhang, Wei Emma, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2018 Número de páginas: X, 231 p. 58 ilustraciones ISBN/ISSN/DL: 978-3-319-76587-7 Nota general: Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Idioma : Inglés (eng) Palabras clave: Software de la aplicacion Servicios de información empresarial TecnologÃa de la información IngenierÃa de software Aplicaciones informáticas y de sistemas de información Sistemas de Información Empresarial Aplicación Informática en Tratamiento de Datos Administrativos Infraestructura de TI empresarial Clasificación: 005.3 Ciencia de los computadores (Programas) Resumen: Este libro constituye una selección revisada de artÃculos del Simposio de Australasia sobre Investigación e Innovación en Servicios, ASSRI, celebrado en Sydney, Australia. Los 11 artÃculos completos presentados en ASSRI 2017, que tuvo lugar del 19 al 20 de octubre de 2017, fueron cuidadosamente revisados ​​y seleccionados entre 26 presentaciones. El volumen también contiene 3 artÃculos de ASSRI 2015, que tuvo lugar del 2 al 3 de noviembre de 2015, y un artÃculo invitado sobre los procesos de desarrollo de software. Las ponencias se organizaron en secciones temáticas denominadas: charla invitada; modelado; diseño; calidad; sociales y de aplicación. Nota de contenido: Invited Talk -- Big Data Analytics Has Little to Do with Analytics -- Modelling -- Accommodating Information Priority Model in Cloudlet Environment -- Learning Planning Model for Semantic Process Compensation -- Design -- Information Systems as a Service (ISaaS): Consumer Co-creation of Value -- Scalable Architecture for Personalized Healthcare Service Recommendation using Big Data Lake -- Declarative Approaches for Compliance by Design -- Quality -- Auction-based Models for Composite Service Selection: A Design Framework -- A Game-theoretic Approach to Quality Improvement in Crowdsourcing Tasks -- Investigating Performance Metrics for Evaluation of Content Delivery Networks -- Social -- Toward Unified Cloud Service Discovery for Enhanced Service Identification -- Predicting issues for resolving in the next release -- Trust and Privacy Challenges in Social Participatory Networks -- Application -- Relating SOA Governance to IT Governance and EA Governance -- Semantic Textual Similarity as a Service -- Logistics and Supply Chain Management Investigation: A Case Study. Tipo de medio : Computadora Summary : This book constitutes revised selected papers from the Australasian Symposium on Service Research and Innovation, ASSRI, held in Sydney Australia. The 11 full papers presented from ASSRI 2017, which took place during October 19-20, 2017, were carefully reviewed and selected from 26 submissions. The volume also contains 3 papers from ASSRI 2015, which took place during November 2-3, 2015, and one invited paper on the software development processes. The papers were organized in topical sections named: invited talk; modelling; design; quality; social, and application. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Service Research and Innovation : 5th and 6th Australasian Symposium, ASSRI 2015 and ASSRI 2017, Sydney, NSW, Australia, November 2–3, 2015, and October 19–20, 2017, Revised Selected Papers / [documento electrónico] / Beheshti, Amin, ; Hashmi, Mustafa, ; Dong, Hai, ; Zhang, Wei Emma, . - 1 ed. . - [s.l.] : Springer, 2018 . - X, 231 p. 58 ilustraciones.
ISBN : 978-3-319-76587-7
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
Idioma : Inglés (eng)
Palabras clave: Software de la aplicacion Servicios de información empresarial TecnologÃa de la información IngenierÃa de software Aplicaciones informáticas y de sistemas de información Sistemas de Información Empresarial Aplicación Informática en Tratamiento de Datos Administrativos Infraestructura de TI empresarial Clasificación: 005.3 Ciencia de los computadores (Programas) Resumen: Este libro constituye una selección revisada de artÃculos del Simposio de Australasia sobre Investigación e Innovación en Servicios, ASSRI, celebrado en Sydney, Australia. Los 11 artÃculos completos presentados en ASSRI 2017, que tuvo lugar del 19 al 20 de octubre de 2017, fueron cuidadosamente revisados ​​y seleccionados entre 26 presentaciones. El volumen también contiene 3 artÃculos de ASSRI 2015, que tuvo lugar del 2 al 3 de noviembre de 2015, y un artÃculo invitado sobre los procesos de desarrollo de software. Las ponencias se organizaron en secciones temáticas denominadas: charla invitada; modelado; diseño; calidad; sociales y de aplicación. Nota de contenido: Invited Talk -- Big Data Analytics Has Little to Do with Analytics -- Modelling -- Accommodating Information Priority Model in Cloudlet Environment -- Learning Planning Model for Semantic Process Compensation -- Design -- Information Systems as a Service (ISaaS): Consumer Co-creation of Value -- Scalable Architecture for Personalized Healthcare Service Recommendation using Big Data Lake -- Declarative Approaches for Compliance by Design -- Quality -- Auction-based Models for Composite Service Selection: A Design Framework -- A Game-theoretic Approach to Quality Improvement in Crowdsourcing Tasks -- Investigating Performance Metrics for Evaluation of Content Delivery Networks -- Social -- Toward Unified Cloud Service Discovery for Enhanced Service Identification -- Predicting issues for resolving in the next release -- Trust and Privacy Challenges in Social Participatory Networks -- Application -- Relating SOA Governance to IT Governance and EA Governance -- Semantic Textual Similarity as a Service -- Logistics and Supply Chain Management Investigation: A Case Study. Tipo de medio : Computadora Summary : This book constitutes revised selected papers from the Australasian Symposium on Service Research and Innovation, ASSRI, held in Sydney Australia. The 11 full papers presented from ASSRI 2017, which took place during October 19-20, 2017, were carefully reviewed and selected from 26 submissions. The volume also contains 3 papers from ASSRI 2015, which took place during November 2-3, 2015, and one invited paper on the software development processes. The papers were organized in topical sections named: invited talk; modelling; design; quality; social, and application. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
