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Author: Laleh Jalali
Publisher:
ISBN: 9781369174045
Category :
Languages : en
Pages : 183
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Book Description
With the proliferation of sensor data, a critical challenge is to interpret and extract knowledge from large-scale heterogeneous observational data. Most knowledge discovery frameworks relay on data mining techniques to extract interesting patterns. The problem of finding such patterns is NP-complete and the property of interestingness is not monotone since a pattern may be interesting, even if its subpatterns are not. In this dissertation a framework for interactive knowledge discovery from heterogeneous high-dimensional temporal data is presented. First, a high-level pattern formulation language is introduced. The language consists of an event model for fusing and abstracting data streams, a semi-interval time model for effectively representing temporal relations, and a set of expressive operators. Based on these operators, a visual and interactive framework is proposed which combines data-driven (bottom-up) and hypothesis-driven (top-down) analyses.This framework takes advantage of data-driven operators for pattern mining and investigating unknown unknowns to generate a basic model and derive a preliminary knowledge. It also uses domain expert knowledge to guide the process of revealing known unknowns. An expert can seed a hypothesis, based on prior knowledge or the knowledge derived from data-driven analysis, and grow it interactively using hypothesis-driven operators. In the context of the pattern mining component, novel time efficient algorithms are introduced which allow discovery of hidden event co-occurrences from multiple event streams. A prototype of the framework is implemented as a web based system which can be utilized as an effective tool for explanation and decision making in almost all disciplines. The applicability of this framework is evaluated in a healthcare application for asthma risk management and a human behavior understanding application, called Objective Self. These applications and experiments highlight the actionable knowledge that the framework can help uncover.
Author: Laleh Jalali
Publisher:
ISBN: 9781369174045
Category :
Languages : en
Pages : 183
Get Book
Book Description
With the proliferation of sensor data, a critical challenge is to interpret and extract knowledge from large-scale heterogeneous observational data. Most knowledge discovery frameworks relay on data mining techniques to extract interesting patterns. The problem of finding such patterns is NP-complete and the property of interestingness is not monotone since a pattern may be interesting, even if its subpatterns are not. In this dissertation a framework for interactive knowledge discovery from heterogeneous high-dimensional temporal data is presented. First, a high-level pattern formulation language is introduced. The language consists of an event model for fusing and abstracting data streams, a semi-interval time model for effectively representing temporal relations, and a set of expressive operators. Based on these operators, a visual and interactive framework is proposed which combines data-driven (bottom-up) and hypothesis-driven (top-down) analyses.This framework takes advantage of data-driven operators for pattern mining and investigating unknown unknowns to generate a basic model and derive a preliminary knowledge. It also uses domain expert knowledge to guide the process of revealing known unknowns. An expert can seed a hypothesis, based on prior knowledge or the knowledge derived from data-driven analysis, and grow it interactively using hypothesis-driven operators. In the context of the pattern mining component, novel time efficient algorithms are introduced which allow discovery of hidden event co-occurrences from multiple event streams. A prototype of the framework is implemented as a web based system which can be utilized as an effective tool for explanation and decision making in almost all disciplines. The applicability of this framework is evaluated in a healthcare application for asthma risk management and a human behavior understanding application, called Objective Self. These applications and experiments highlight the actionable knowledge that the framework can help uncover.
Author: Wee Keong Ng
Publisher: Springer Science & Business Media
ISBN: 3540332065
Category : Computers
Languages : en
Pages : 902
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Book Description
This book constitutes the refereed proceedings of the 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2006, held in Singapore in April 2006. The 67 revised full papers and 33 revised short papers presented together with 3 invited talks were carefully reviewed and selected from 501 submissions. The papers are organized in topical sections on Classification, Ensemble Learning, Clustering, Support Vector Machines, Text and Document Mining, Web Mining, Bio-Data Mining, and more.
Author: Joao Gama
Publisher: CRC Press
ISBN: 1439826129
Category : Business & Economics
Languages : en
Pages : 256
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Book Description
Since the beginning of the Internet age and the increased use of ubiquitous computing devices, the large volume and continuous flow of distributed data have imposed new constraints on the design of learning algorithms. Exploring how to extract knowledge structures from evolving and time-changing data, Knowledge Discovery from Data Streams presents
Author: Massih-Reza Amini
Publisher: Springer Nature
ISBN: 3031264223
Category : Computers
Languages : en
Pages : 712
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Book Description
The multi-volume set LNAI 13713 until 13718 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2022, which took place in Grenoble, France, in September 2022. The 236 full papers presented in these proceedings were carefully reviewed and selected from a total of 1060 submissions. In addition, the proceedings include 17 Demo Track contributions. The volumes are organized in topical sections as follows: Part I: Clustering and dimensionality reduction; anomaly detection; interpretability and explainability; ranking and recommender systems; transfer and multitask learning; Part II: Networks and graphs; knowledge graphs; social network analysis; graph neural networks; natural language processing and text mining; conversational systems; Part III: Deep learning; robust and adversarial machine learning; generative models; computer vision; meta-learning, neural architecture search; Part IV: Reinforcement learning; multi-agent reinforcement learning; bandits and online learning; active and semi-supervised learning; private and federated learning; . Part V: Supervised learning; probabilistic inference; optimal transport; optimization; quantum, hardware; sustainability; Part VI: Time series; financial machine learning; applications; applications: transportation; demo track.
Author: Chengfei Liu
Publisher: Springer
ISBN: 3319914553
Category : Computers
Languages : en
Pages : 282
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Book Description
This book constitutes the workshop proceedings of the 23rd International Conference on Database Systems for Advanced Applications, DASFAA 2018, held in Gold Coast, QLD, Australia, in May 2018. The 23 full papers presented were carefully selected and reviewed from 44 submissions to the four following workshops: the 5th International Workshop on Big Data Management and Service, BDMS 2018; the Third International Workshop on Big Data Quality Management, BDQM 2018; the Second International Workshop on Graph Data Management and Analysis, GDMA 2018; and the 5th International Workshop on Semantic Computing and Personalization, SeCoP 2018.
Author: João Gama
Publisher: Springer Science & Business Media
ISBN: 3540736786
Category : Computers
Languages : en
Pages : 486
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Book Description
Processing data streams has raised new research challenges over the last few years. This book provides the reader with a comprehensive overview of stream data processing, including famous prototype implementations like the Nile system and the TinyOS operating system. Applications in security, the natural sciences, and education are presented. The huge bibliography offers an excellent starting point for further reading and future research.
Author: M. Costantino
Publisher: WIT Press
ISBN: 1845641744
Category : Business & Economics
Languages : en
Pages : 449
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Book Description
Featuring papers from the Second International Conference on Computational Finance and its Applications, the text includes papers that encompass a wide range of topics such as risk management, derivatives pricing, credit risk, trading strategies, portfolio management and asset allocation, and market analysis.
Author: Paolo Ceravolo
Publisher: Springer
ISBN: 3319534351
Category : Computers
Languages : en
Pages : 195
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Book Description
This book constitutes the revised selected papers from the 5th IFIP WG 2.6 International Symposium on Data-Driven Process Discovery and Analysis, SIMPDA 2015, held in Vienna, Austria in December 2015. The 8 papers presented in this volume were carefully reviewed and selected from 22 submissions. They cover theoretical issues related to process representation, discovery and analysis, or provide practical and operational experiences in process discovery and analysis. They focus mainly on the adoption of process mining algorithms in conjunction and coordination with other techniques and methodologies.
Author: Ritu Arora
Publisher: Springer
ISBN: 3319337424
Category : Computers
Languages : en
Pages : 329
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Book Description
This book provides an overview of the resources and research projects that are bringing Big Data and High Performance Computing (HPC) on converging tracks. It demystifies Big Data and HPC for the reader by covering the primary resources, middleware, applications, and tools that enable the usage of HPC platforms for Big Data management and processing.Through interesting use-cases from traditional and non-traditional HPC domains, the book highlights the most critical challenges related to Big Data processing and management, and shows ways to mitigate them using HPC resources. Unlike most books on Big Data, it covers a variety of alternatives to Hadoop, and explains the differences between HPC platforms and Hadoop.Written by professionals and researchers in a range of departments and fields, this book is designed for anyone studying Big Data and its future directions. Those studying HPC will also find the content valuable.
Author: Peggy Cellier
Publisher: Springer Nature
ISBN: 3030438236
Category : Computers
Languages : en
Pages : 688
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Book Description
This two-volume set constitutes the refereed proceedings of the workshops which complemented the 19th Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD, held in Würzburg, Germany, in September 2019. The 70 full papers and 46 short papers presented in the two-volume set were carefully reviewed and selected from 200 submissions. The two volumes (CCIS 1167 and CCIS 1168) present the papers that have been accepted for the following workshops: Workshop on Automating Data Science, ADS 2019; Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence and eXplainable Knowledge Discovery in Data Mining, AIMLAI-XKDD 2019; Workshop on Decentralized Machine Learning at the Edge, DMLE 2019; Workshop on Advances in Managing and Mining Large Evolving Graphs, LEG 2019; Workshop on Data and Machine Learning Advances with Multiple Views; Workshop on New Trends in Representation Learning with Knowledge Graphs; Workshop on Data Science for Social Good, SoGood 2019; Workshop on Knowledge Discovery and User Modelling for Smart Cities, UMCIT 2019; Workshop on Data Integration and Applications Workshop, DINA 2019; Workshop on Machine Learning for Cybersecurity, MLCS 2019; Workshop on Sports Analytics: Machine Learning and Data Mining for Sports Analytics, MLSA 2019; Workshop on Categorising Different Types of Online Harassment Languages in Social Media; Workshop on IoT Stream for Data Driven Predictive Maintenance, IoTStream 2019; Workshop on Machine Learning and Music, MML 2019; Workshop on Large-Scale Biomedical Semantic Indexing and Question Answering, BioASQ 2019. The chapter "Supervised Human-guided Data Exploration" is published open access under a Creative Commons Attribution 4.0 International license (CC BY).
