Download or read book A Method to Detect and Represent Temporal Patterns from Time Series Data and Its Application for Analysis of Physiological Data Streams written by Catherine Inibhunu and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In critical care, complex systems and sensors continuously monitor patients' physiological features such as heart rate, respiratory rate thus generating significant amounts of data every second. This results to more than 2 million records generated per patient in an hour. It's an immense challenge for anyone trying to utilize this data when making critical decisions about patient care. Temporal abstraction and data mining are two research fields that have tried to synthesize time oriented data to detect hidden relationships that may exist in the data. Various researchers have looked at techniques for generating abstractions from clinical data. However, the variety and speed of data streams generated often overwhelms current systems which are not designed to handle such data. Other attempts have been to understand the complexity in time series data utilizing mining techniques, however, existing models are not designed to detect temporal relationships that might exist in time series data (Inibhunu & McGregor, 2016). To address this challenge, this thesis has proposed a method that extends the existing knowledge discovery frameworks to include components for detecting and representing temporal relationships in time series data. The developed method is instantiated within the knowledge discovery component of Artemis, a cloud based platform for processing physiological data streams. This is a unique approach that utilizes pattern recognition principles to facilitate functions for; (a) temporal representation of time series data with abstractions, (b) temporal pattern generation and quantification (c) frequent patterns identification and (d) building a classification system. This method is applied to a neonatal intensive care case study with a motivating problem that discovery of specific patterns from patient data could be crucial for making improved decisions within patient care. Another application is in chronic care to detect temporal relationships in ambulatory patient data before occurrence of an adverse event. The research premise is that discovery of hidden relationships and patterns in data would be valuable in building a classification system that automatically characterize physiological data streams. Such characterization could aid in detection of new normal and abnormal behaviors in patients who may have life threatening conditions.
Download or read book Recent Advances in Knowledge Management written by Muhammad Mohiuddin and published by BoD – Books on Demand. This book was released on 2022-10-19 with total page 180 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent Advances in Knowledge Management investigates the multidimensional aspects of knowledge management by exploring different perspectives and practices as well as existing theories of effective knowledge management in a changing world. Chapters address such topics as tacit knowledge, knowledge management frameworks, informally structured domains of knowledge management, and more. Beyond understanding the nature of knowledge management processes in different kinds of organizations, this book examines the nature of knowledge management focusing on what we know and how we know it.
Download or read book Time Series Analysis written by Chun-Kit Ngan and published by BoD – Books on Demand. This book was released on 2019-11-06 with total page 131 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book aims to provide readers with the current information, developments, and trends in a time series analysis, particularly in time series data patterns, technical methodologies, and real-world applications. This book is divided into three sections and each section includes two chapters. Section 1 discusses analyzing multivariate and fuzzy time series. Section 2 focuses on developing deep neural networks for time series forecasting and classification. Section 3 describes solving real-world domain-specific problems using time series techniques. The concepts and techniques contained in this book cover topics in time series research that will be of interest to students, researchers, practitioners, and professors in time series forecasting and classification, data analytics, machine learning, deep learning, and artificial intelligence.
Download or read book A Distributed Architecture for the Monitoring and Analysis of Time Series Data written by Ruairi O'Reilly and published by Ruairí O'Reilly. This book was released on with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract It is estimated that the quantity of digital data being transferred, processed or stored at any one time currently stands at 4.4 zettabytes (4.4 × 270 bytes) and this figure is expected to have grown by a factor of 10 to 44 zettabytes by 2020 [1]. Exploiting this data is and will remain, a significant challenge. At present there is the capacity to store 33% of digital data in existence at any one time; by 2020 this capacity is expected to fall to 15%. These statistics suggest that, in the era of Big Data, the identification of important, exploitable data will need to be done in a timely manner. Systems for the monitoring and analysis of data, e.g. stock markets, smart grids and sensor networks, can be made up of massive numbers of individual components. These components can be geographically distributed yet may interact with one another via continuous data streams, which in turn may affect the state of the sender or receiver. This introduces a dynamic causality, which further complicates the overall system by introducing a temporal constraint that is difficult to accommodate. Practical approaches to realising the system described above have led to a multiplicity of analysis techniques, each of which concentrates on specific characteristics of the system being analysed and treats these characteristics as the dominant component affecting the results being sought. The multiplicity of analysis techniques introduces another layer of heterogeneity, that is heterogeneity of approach, partitioning the field to the extent that results from one domain are difficult to exploit in another. The question is asked can a generic solution for the monitoring and analysis of data that: accommodates temporal constraints; bridges the gap between expert knowledge and raw data; and enables data to be effectively interpreted and exploited in a transparent manner, be identified? The approach proposed in this dissertation acquires, analyses and processes data in a manner that is free of the constraints of any particular analysis technique, while at the same time facilitating these techniques where appropriate. Constraints are applied by defining a workflow based on the production, interpretation and consumption of data. This supports the application of different analysis techniques on the same raw data without the danger of incorporating hidden bias that may exist. To illustrate and to realise this approach a software platform has been created that allows for the transparent analysis of data, combining analysis techniques with a maintainable record of provenance so that independent third party analysis can be applied to verify any derived conclusions. In order to demonstrate these concepts, a complex real-world example involving the near real-time capturing and analysis of neurophysiological data from a neonatal intensive care unit (NICU) was chosen. A system was engineered to gather raw data, analyse that data using different analysis techniques, uncover information, incorporate that information into the system and curate the evolution of the discovered knowledge. The application domain was chosen for three reasons: firstly because it is complex and no comprehensive solution exists; secondly, it requires tight interaction with domain experts, thus requiring the handling of subjective knowledge and inference; and thirdly, given the dearth of neurophysiologists, there is a real-world need to provide a solution for this domain.
Download or read book Spectral Analysis of Time series Data written by Rebecca M. Warner and published by Guilford Press. This book was released on 1998-05-22 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a thorough introduction to methods for detecting and describing cyclic patterns in time-series data. It is written both for researchers and students new to the area and for those who have already collected time-series data but wish to learn new ways of understanding and presenting them. Facilitating the interpretation of observations of behavior, physiology, mood, perceptual threshold, social indicator variables, and other responses, the book focuses on practical applications and requires much less mathematical background than most comparable texts. Using real data sets and currently available software (SPSS for Windows), the author employs extensive examples to clarify key concepts. Topics covered include research design issues, preliminary data screening, identification and description of cycles, summary of results across time series, and assessment of relations between time series. Also considered are theoretical questions, problems of interpretation, and potential sources of artifact.
Download or read book Online Monitoring and Prediction of Complex Time Series Events from Nonstationary Time Series Data written by Shouyi Wang and published by . This book was released on 2012 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: Much of the world's supply of data is in the form of time series. In the last decade, there has been an explosion of interest in time series data mining. Time series prediction has been widely used in engineering, economy, industrial manufacturing, finance, manage- ment and many other fields. Many new algorithms have been developed to classify, cluster, segment, index, discover rules, and detect anomalies/novelties in time series. However, traditional time series analysis methods are limited by the requirement of stationarity of the time series and normality and independence of the residuals. Be- cause they attempt to characterize and predict all time series observations, traditional time series analysis methods are unable to identify complex (nonperiodic, nonlinear, irregular, and chaotic) characteristics. As a result, the prediction of multivariate noisy time series (such as physiological signals) is still very challenging due to high noise, non-stationarity, and non-linearity. The objective of this research is to develop new reliable frameworks for analyzing multivariate noisy time series, and to apply the framework to online monitor noisy time series and predict critical events online. In particular, this research made an extensive study on one important form of multivariate time series: electrocorticogram (EEG) data, based on which two new online monitoring and prediction frameworks for multivariate time series were introduced and evaluated. The new online monitoring and prediction frameworks overcome the limitations of traditional time series analysis techniques, and adapt and innovate data mining concepts to analyzing multivariate time series data. The proposed approaches can be general frameworks to create a set of methods that reveal hidden temporal patterns that are characteristic and predictive of time series events. In second part of this dissertation provide an overview of the state-of-the-art pre- diction approaches. In the third part of this dissertation, we perform an extensive data mining study on multivariate EEG data, which indicates that EEG may be predictable for some events. In chapter 4, a reinforcement learning-based online monitoring and prediction framework is introduced and applied to solve the challenging seizure pre- diction problem from multivariate EEG data. In chapter 5, it first overview of the most popular representation methods for time series data, and then introduce two new robust algorithms for offline and online segmentation of a time series, respectively. Chapter 6 proposes a general online monitoring and prediction framework, which com- bines temporal feature extraction, feature selection, online pattern identification, and adaptive learning theory to achieve online prediction of complex time series events. Two prediction-rule construction schemes are proposed. In chapter 7, the proposed framework is applied to solve two challenging problems including seizure prediction and 'anxiety' prediction in a simulated driving environment. The significant prediction results demonstrated the superior prediction capability of the proposed framework to predict complex target events from online streams of nonstationary and chaotic time series.
Download or read book Artificial Intelligence in Medicine written by David Riaño and published by Springer. This book was released on 2019-06-19 with total page 431 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 17th Conference on Artificial Intelligence in Medicine, AIME 2019, held in Poznan, Poland, in June 2019. The 22 revised full and 31 short papers presented were carefully reviewed and selected from 134 submissions. The papers are organized in the following topical sections: deep learning; simulation; knowledge representation; probabilistic models; behavior monitoring; clustering, natural language processing, and decision support; feature selection; image processing; general machine learning; and unsupervised learning.
Download or read book Ubiquitous and Pervasive Computing Concepts Methodologies Tools and Applications written by Symonds, Judith and published by IGI Global. This book was released on 2009-09-30 with total page 1962 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This publication covers the latest innovative research findings involved with the incorporation of technologies into everyday aspects of life"--Provided by publisher.
Download or read book Advanced Analytics and Learning on Temporal Data written by Vincent Lemaire and published by Springer Nature. This book was released on 2020-01-22 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 4th ECML PKDD Workshop on Advanced Analytics and Learning on Temporal Data, AALTD 2019, held in Würzburg, Germany, in September 2019. The 7 full papers presented together with 9 poster papers were carefully reviewed and selected from 31 submissions. The papers cover topics such as temporal data clustering; classification of univariate and multivariate time series; early classification of temporal data; deep learning and learning representations for temporal data; modeling temporal dependencies; advanced forecasting and prediction models; space-temporal statistical analysis; functional data analysis methods; temporal data streams; interpretable time-series analysis methods; dimensionality reduction, sparsity, algorithmic complexity and big data challenge; and bio-informatics, medical, energy consumption, on temporal data.
Download or read book Temporal Data Mining written by Theophano Mitsa and published by CRC Press. This book was released on 2010-03-10 with total page 398 pages. Available in PDF, EPUB and Kindle. Book excerpt: From basic data mining concepts to state-of-the-art advances, this book covers the theory of the subject as well as its application in a variety of fields. It discusses the incorporation of temporality in databases as well as temporal data representation, similarity computation, data classification, clustering, pattern discovery, and prediction. The book also explores the use of temporal data mining in medicine and biomedical informatics, business and industrial applications, web usage mining, and spatiotemporal data mining. Along with various state-of-the-art algorithms, each chapter includes detailed references and short descriptions of relevant algorithms and techniques described in other references.
Download or read book Time Series Analysis Modeling and Applications written by Witold Pedrycz and published by Springer Science & Business Media. This book was released on 2012-11-29 with total page 398 pages. Available in PDF, EPUB and Kindle. Book excerpt: Temporal and spatiotemporal data form an inherent fabric of the society as we are faced with streams of data coming from numerous sensors, data feeds, recordings associated with numerous areas of application embracing physical and human-generated phenomena (environmental data, financial markets, Internet activities, etc.). A quest for a thorough analysis, interpretation, modeling and prediction of time series comes with an ongoing challenge for developing models that are both accurate and user-friendly (interpretable). The volume is aimed to exploit the conceptual and algorithmic framework of Computational Intelligence (CI) to form a cohesive and comprehensive environment for building models of time series. The contributions covered in the volume are fully reflective of the wealth of the CI technologies by bringing together ideas, algorithms, and numeric studies, which convincingly demonstrate their relevance, maturity and visible usefulness. It reflects upon the truly remarkable diversity of methodological and algorithmic approaches and case studies. This volume is aimed at a broad audience of researchers and practitioners engaged in various branches of operations research, management, social sciences, engineering, and economics. Owing to the nature of the material being covered and a way it has been arranged, it establishes a comprehensive and timely picture of the ongoing pursuits in the area and fosters further developments.
Download or read book Change Detection and Image Time Series Analysis 2 written by Abdourrahmane M. Atto and published by John Wiley & Sons. This book was released on 2021-12-29 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt: Change Detection and Image Time Series Analysis 2 presents supervised machine-learning-based methods for temporal evolution analysis by using image time series associated with Earth observation data. Chapter 1 addresses the fusion of multisensor, multiresolution and multitemporal data. It proposes two supervised solutions that are based on a Markov random field: the first relies on a quad-tree and the second is specifically designed to deal with multimission, multifrequency and multiresolution time series. Chapter 2 provides an overview of pixel based methods for time series classification, from the earliest shallow learning methods to the most recent deep-learning-based approaches. Chapter 3 focuses on very high spatial resolution data time series and on the use of semantic information for modeling spatio-temporal evolution patterns. Chapter 4 centers on the challenges of dense time series analysis, including pre processing aspects and a taxonomy of existing methodologies. Finally, since the evaluation of a learning system can be subject to multiple considerations, Chapters 5 and 6 offer extensive evaluations of the methodologies and learning frameworks used to produce change maps, in the context of multiclass and/or multilabel change classification issues.
Download or read book High Performance Discovery In Time Series written by Dennis Elliott Shasha and published by Springer Science & Business Media. This book was released on 2004-06-03 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt: Time-series data—data arriving in time order, or a data stream—can be found in fields such as physics, finance, music, networking, and medical instrumentation. Designing fast, scalable algorithms for analyzing single or multiple time series can lead to scientific discoveries, medical diagnoses, and perhaps profits. High Performance Discovery in Time Series presents rapid-discovery techniques for finding portions of time series with many events (i.e., gamma-ray scatterings) and finding closely related time series (i.e., highly correlated price and return histories, or musical melodies). A typical time-series technique may compute a "consensus" time series—from a collection of time series—to use regression analysis for predicting future time points. By contrast, this book aims at efficient discovery in time series, rather than prediction, and its novelty lies in its algorithmic contributions and its simple, practical algorithms and case studies. It presumes familiarity with only basic calculus and some linear algebra. Topics and Features: *Presents efficient algorithms for discovering unusual bursts of activity in large time-series databases * Describes the mathematics and algorithms for finding correlation relationships between thousands or millions of time series across fixed or moving windows *Demonstrates strong, relevant applications built on a solid scientific basis *Outlines how readers can adapt the techniques for their own needs and goals *Describes algorithms for query by humming, gamma-ray burst detection, pairs trading, and density detection *Offers self-contained descriptions of wavelets, fast Fourier transforms, and sketches as they apply to time-series analysis This new monograph provides a technical survey of concepts and techniques for describing and analyzing large-scale time-series data streams. It offers essential coverage of the topic for computer scientists, physicists, medical researchers, financial mathematicians, musicologists, and researchers and professionals who must analyze massive time series. In addition, it can serve as an ideal text/reference for graduate students in many data-rich disciplines.
Download or read book Handbook of Time Series Analysis written by Björn Schelter and published by John Wiley & Sons. This book was released on 2006-12-13 with total page 514 pages. Available in PDF, EPUB and Kindle. Book excerpt: This handbook provides an up-to-date survey of current research topics and applications of time series analysis methods written by leading experts in their fields. It covers recent developments in univariate as well as bivariate and multivariate time series analysis techniques ranging from physics' to life sciences' applications. Each chapter comprises both methodological aspects and applications to real world complex systems, such as the human brain or Earth's climate. Covering an exceptionally broad spectrum of topics, beginners, experts and practitioners who seek to understand the latest developments will profit from this handbook.
Download or read book Multivariate Time Series Analysis of Physiological and Clinical Data written by Patricia Ordonez and published by . This book was released on 2012 with total page 460 pages. Available in PDF, EPUB and Kindle. Book excerpt: The complexity and volume of collected medical data is greater now than at any point in the history of medicine. Medical providers are expected to examine large volumes of data and identify correlations among parameters based on their own clinical experience to detect significant medical events or conditions. The information overload that providers may face may hinder the diagnostic process cite{heldt2006}. Most existing visualizations of the data to assist the provider in analyzing the information consist of a table or plot of values for a particular parameter as a function of time. Automated techniques for discovering these correlations not only may assist the provider in making a diagnosis but may help to identify hidden patterns within the data associated with specific medical conditions or events. Current visualization and machine learning techniques show promise for extracting this information.
Download or read book Identification of Sequential Changes and Investigation of Change Patterns in Dynamic Spatiotemporal Data written by Eunjung LIM and published by . This book was released on 2009 with total page 286 pages. Available in PDF, EPUB and Kindle. Book excerpt: Spatiotemporal data generally undergo fluctuations. Identifying a sequence of changes in spatiotemporal data and investigating patterns of the detected changes are commonly required across many domains for analysis, prediction, and decision-making. However, most current analytical tools do not support both functions, so there has been a limitation in comprehensively analyzing dynamics in ongoing spatiotemporal data. This research suggests an integrated change analysis framework to support the overall analysis process, from detecting a sequence of changes to investigating patterns of the detected changes, as a prototype of a decision support tool. To identify a sequence of multiple changes in ongoing spatiotemporal data, where the spatiotemporal process is unknown, this research develops a recursive change detection procedure that applies a nonparametric cumulative sum method designed to signal a shift repeatedly.^The recursive change detection procedure diagnoses and estimates the actual change-point following a signal of a shift and uses the mean and variance of all observations accumulated from the estimated actual shift as the subsequent in-control mean and variance for detecting the next change. After detecting a sequence of changes, the identified changes are interpreted as explicit change types by domain-dependent definition of the change types. Spatial and/or temporal patterns of explicit changes are visualized on a set of event bands by flexibly assigning controlled dimensions to event bands, stacks, and panels, which support investigating similarities and differences of explicit changes according to the controlled space and/or time. The proposed integrated analysis of sequential changes in spatiotemporal data is applied to assist emergency facility location planning coping with time-varying demand (i.e., emergency call) patterns.^A total of 12, 11, 11, and 9 sequential changes in spatial patterns of the daily emergency calls in Buffalo are identified by the recursive change detection procedure for 1998, 1999, 2000, and 2001, respectively. Spatial and temporal similarities and differences of local clusters (hot spots and cold spots) and local outliers before and after each change are investigated to obtain valuable information for location planning. A set of scenarios describing different patterns of demands and probability of each scenario are specified by exploring spatial and temporal patterns of hot spots and cold spots on event band and stack structures. Timing of relocation is identified by investigating temporal similarities of spatial demand patterns contrary to dominant spatial patterns on set of event bands. Thus, useful inputs and strategies are provided for stochastic and dynamic location planning through the integrated analysis of sequential changes in emergency calls.^In this context, the integrated change analysis framework proposed in this research has the potential to support policy decisions in various service fields where analysis of dynamics in spatiotemporal data is useful.
Download or read book Foundations of Augmented Cognition written by Dylan D. Schmorrow and published by Springer. This book was released on 2013-06-12 with total page 810 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 5th International Conference on Augmented Cognition, AC 2013, held as part of the 15th International Conference on Human-Computer Interaction, HCII 2013, held in Las Vegas, USA in July 2013, jointly with 12 other thematically similar conferences. The total of 1666 papers and 303 posters presented at the HCII 2013 conferences was carefully reviewed and selected from 5210 submissions. These papers address the latest research and development efforts and highlight the human aspects of design and use of computing systems. The papers accepted for presentation thoroughly cover the entire field of human-computer interaction, addressing major advances in knowledge and effective use of computers in a variety of application areas. The total of 81 contributions was carefully reviewed and selected for inclusion in the AC proceedings. The papers are organized in the following topical sections: augmented cognition in training and education; team cognition; brain activity measurement; understanding and modeling cognition; cognitive load, stress and fatigue; applications of augmented cognition.
