Data Abstraction And Pattern Identification In Time Series Data
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Author | : Prithiviraj Muthumanickam |
Publisher | : Linköping University Electronic Press |
Total Pages | : 58 |
Release | : 2019-11-25 |
Genre | : |
ISBN | : 9179299652 |
Data sources such as simulations, sensor networks across many application domains generate large volumes of time-series data which exhibit characteristics that evolve over time. Visual data analysis methods can help us in exploring and understanding the underlying patterns present in time-series data but, due to their ever-increasing size, the visual data analysis process can become complex. Large data sets can be handled using data abstraction techniques by transforming the raw data into a simpler format while, at the same time, preserving significant features that are important for the user. When dealing with time-series data, abstraction techniques should also take into account the underlying temporal characteristics. This thesis focuses on different data abstraction and pattern identification methods particularly in the cases of large 1D time-series and 2D spatio-temporal time-series data which exhibit spatiotemporal discontinuity. Based on the dimensionality and characteristics of the data, this thesis proposes a variety of efficient data-adaptive and user-controlled data abstraction methods that transform the raw data into a symbol sequence. The transformation of raw time-series into a symbol sequence can act as input to different sequence analysis methods from data mining and machine learning communities to identify interesting patterns of user behavior. In the case of very long duration 1D time-series, locally adaptive and user-controlled data approximation methods were presented to simplify the data, while at the same time retaining the perceptually important features. The simplified data were converted into a symbol sequence and a sketch-based pattern identification was then used to identify patterns in the symbolic data using regular expression based pattern matching. The method was applied to financial time-series and patterns such as head-and-shoulders, double and triple-top patterns were identified using hand drawn sketches in an interactive manner. Through data smoothing, the data approximation step also enables visualization of inherent patterns in the time-series representation while at the same time retaining perceptually important points. Very long duration 2D spatio-temporal eye tracking data sets that exhibit spatio-temporal discontinuity was transformed into symbolic data using scalable clustering and hierarchical cluster merging processes, each of which can be parallelized. The raw data is transformed into a symbol sequence with each symbol representing a region of interest in the eye gaze data. The identified regions of interest can also be displayed in a Space-Time Cube (STC) that captures both the temporal and contextual information. Through interactive filtering, zooming and geometric transformation, the STC representation along with linked views enables interactive data exploration. Using different sequence analysis methods, the symbol sequences are analyzed further to identify temporal patterns in the data set. Data collected from air traffic control officers from the domain of Air traffic control were used as application examples to demonstrate the results.
Author | : Richard O. Duda |
Publisher | : John Wiley & Sons |
Total Pages | : 680 |
Release | : 2012-11-09 |
Genre | : Technology & Engineering |
ISBN | : 111858600X |
The first edition, published in 1973, has become a classicreference in the field. Now with the second edition, readers willfind information on key new topics such as neural networks andstatistical pattern recognition, the theory of machine learning,and the theory of invariances. Also included are worked examples,comparisons between different methods, extensive graphics, expandedexercises and computer project topics. An Instructor's Manual presenting detailed solutions to all theproblems in the book is available from the Wiley editorialdepartment.
Author | : Mayank Singh |
Publisher | : Springer |
Total Pages | : 597 |
Release | : 2018-10-25 |
Genre | : Computers |
ISBN | : 9811318131 |
This two-volume set (CCIS 905 and CCIS 906) constitutes the refereed proceedings of the Second International Conference on Advances in Computing and Data Sciences, ICACDS 2018, held in Dehradun, India, in April 2018. The 110 full papers were carefully reviewed and selected from 598 submissions. The papers are centered around topics like advanced computing, data sciences, distributed systems organizing principles, development frameworks and environments, software verification and validation, computational complexity and cryptography, machine learning theory, database theory, probabilistic representations.
Author | : |
Publisher | : Allied Publishers |
Total Pages | : 228 |
Release | : 2010 |
Genre | : Database management |
ISBN | : 9788184246452 |
Author | : Leandro N. de Castro |
Publisher | : Springer Science & Business Media |
Total Pages | : 449 |
Release | : 2007-08-07 |
Genre | : Computers |
ISBN | : 3540739211 |
This book constitutes the refereed proceedings of the 6th International Conference on Artificial Immune Systems, ICARIS 2007, held in Santos, Brazil, in August 2007. The 36 revised full papers presented were carefully reviewed and selected from 58 submissions. The papers are organized in topical sections on search and optimization, classification and clustering, anomaly detection and negative selection, robotics, control and electronics, modeling papers, conceptual papers, as well as technical papers and general applications.
Author | : Arvind Kumar Bansal |
Publisher | : CRC Press |
Total Pages | : 664 |
Release | : 2020-01-08 |
Genre | : Medical |
ISBN | : 1000761592 |
This class-tested textbook is designed for a semester-long graduate or senior undergraduate course on Computational Health Informatics. The focus of the book is on computational techniques that are widely used in health data analysis and health informatics and it integrates computer science and clinical perspectives. This book prepares computer science students for careers in computational health informatics and medical data analysis. Features Integrates computer science and clinical perspectives Describes various statistical and artificial intelligence techniques, including machine learning techniques such as clustering of temporal data, regression analysis, neural networks, HMM, decision trees, SVM, and data mining, all of which are techniques used widely used in health-data analysis Describes computational techniques such as multidimensional and multimedia data representation and retrieval, ontology, patient-data deidentification, temporal data analysis, heterogeneous databases, medical image analysis and transmission, biosignal analysis, pervasive healthcare, automated text-analysis, health-vocabulary knowledgebases and medical information-exchange Includes bioinformatics and pharmacokinetics techniques and their applications to vaccine and drug development
Author | : Antonio Pertusa |
Publisher | : Springer Nature |
Total Pages | : 735 |
Release | : 2023-06-24 |
Genre | : Computers |
ISBN | : 3031366166 |
This book constitutes the refereed proceedings of the 11th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2023, held in Alicante, Spain, in June 27–30, 2023. The 56 papers accepted for these proceedings were carefully reviewed and selected from 86 submissions. They deal with Machine Learning, Document Analysis, Computer Vision, 3D Computer Vision, Computer Vision Applications, Medical Imaging & Applications, Machine Learning Applications.
Author | : Kim Sehun |
Publisher | : Springer |
Total Pages | : 399 |
Release | : 2008-01-09 |
Genre | : Computers |
ISBN | : 3540775358 |
Complete with Springer’s trademark online files and updates, this fascinating text constitutes the refereed proceedings of the 8th International Workshop on Information Security Applications, WISA 2007, held in Jeju Island, Korea, in August 2007. The 27 revised full papers presented were carefully selected during two rounds of reviewing and improvement from 95 submissions. The papers are organized in topical sections on a wide range of subjects from secure systems to P2P security.
Author | : Wolfgang Karl Härdle |
Publisher | : Springer |
Total Pages | : 532 |
Release | : 2018-07-20 |
Genre | : Computers |
ISBN | : 3319182846 |
Addressing a broad range of big data analytics in cross-disciplinary applications, this essential handbook focuses on the statistical prospects offered by recent developments in this field. To do so, it covers statistical methods for high-dimensional problems, algorithmic designs, computation tools, analysis flows and the software-hardware co-designs that are needed to support insightful discoveries from big data. The book is primarily intended for statisticians, computer experts, engineers and application developers interested in using big data analytics with statistics. Readers should have a solid background in statistics and computer science.
Author | : José Francisco Martinez-Trinidad |
Publisher | : Springer Science & Business Media |
Total Pages | : 364 |
Release | : 2011-06-16 |
Genre | : Computers |
ISBN | : 3642215866 |
This book constitutes the refereed proceedings of the Third Mexican Conference on Pattern Recognition, MCPR 2011, held in Cancun, Mexico, in June/July 2011. The 37 revised full papers were carefully reviewed and selected from 69 submissions and are organized in topical sections on pattern recognition and data mining; computer vision and robotics; image processing; neural networks and signal processing; and natural language and document processing.