Compression-based data mining of sequential data

The vast majority of data mining algorithms require the setting of many input parameters. The dangers of working with parameter-laden algorithms are twofold. First, incorrect settings may cause an algorithm to fail in finding the true patterns. Second, a perhaps more insidious problem is that the algorithm may report spurious patterns that do not really exist, or greatly overestimate the significance of the reported patterns. This is especially likely when the user fails to understand the role of parameters in the data mining process. Data mining algorithms should have as few parameters as possible. A parameter-light algorithm would limit our ability to impose our prejudices, expectations, and presumptions on the problem at hand, and would let the data itself speak to us. In this work, we show that recent results in bioinformatics, learning, and computational theory hold great promise for a parameter-light data-mining paradigm. The results are strongly connected to Kolmogorov complexity theory. However, as a practical matter, they can be implemented using any off-the-shelf compression algorithm with the addition of just a dozen lines of code. We will show that this approach is competitive or superior to many of the state-of-the-art approaches in anomaly/interestingness detection, classification, and clustering with empirical tests on time series/DNA/text/XML/video datasets. As a further evidence of the advantages of our method, we will demonstrate its effectiveness to solve a real world classification problem in recommending printing services and products. Responsible editor: Johannes Gehrke     

Efficient query filtering for streaming time series with applications to semisupervised learning of time series classifiers

In this paper, we define time series query filtering, the problem of monitoring the streaming time series for a set of predefined patterns. This problem is of great practical importance given the massive volume of streaming time series available through sensors, medical patient records, financial indices and space telemetry. Since the data may arrive at a high rate and the number of predefined patterns can be relatively large, it may be impossible for the comparison algorithm to keep up. We propose a novel technique that exploits the commonality among the predefined patterns to allow monitoring at higher bandwidths, while maintaining a guarantee of no false dismissals. Our approach is based on the widely used envelope-based lower-bounding technique. As we will demonstrate on extensive experiments in diverse domains, our approach achieves tremendous improvements in performance in the offline case, and significant improvements in the fastest possible arrival rate of the data stream that can be processed with guaranteed no false dismissals. As a further demonstration of the utility of our approach, we demonstrate that it can make semisupervised learning of time series classifiers tractable. Li Wei is a Ph.D. candidate in the Department of Computer Science & Engineering at the University of California, Riverside. She received her B.S. and M.S. degrees from Fudan University, China. Her research interests include data mining and information retrieval. Eamonn Keogh is an Assistant Professor of computer science at the University of California, Riverside. His research interests include data mining, machine learning and information retrieval. Several of his papers have won best paper awards, including papers at SIGKDD and SIGMOD. Dr. Keogh is the recipient of a 5-year NSF Career Award for “Efficient Discovery of Previously Unknown Patterns and Relationships in Massive Time Series Databases”. Helga Van Herle is an Assistant Clinical Professor of medicine at the Division of Cardiology of the Geffen School of Medicine at UCLA. She received her M.D. from UCLA in 1993; completed her residency in internal medicine at the New York Hospital (Cornell University; 1993–1996) and her cardiology fellowship at UCLA (1997–2001). Dr. Van Herle holds an M.Sc. in bioengineering from Columbia University (1987) and a B.Sc. in chemical engineering from UCLA (1985). Agenor Mafra-Neto, Ph.D., is the CEO of ISCA Technologies, Inc., in California and the founder of ISCA Technologies, LTDA, in Brazil. His research interests include the analysis of insect behavior and communication systems, the manipulation of insect behavior, and the automation of pest monitoring and pest control. Dr. Mafra-Neto is currently coordinating the deployment of area-wide smart sensor and effector networks to micromanage agricultural and public health pests in the field in an automatic fashion. Russell J. Abbott is a Professor of computer science at California State University, Los Angeles, and a member of the staff at the Aerospace Corporation, El Segundo, CA. His primary interests are in the field of complex systems. He is currently organizing a workshop to bring together people working in the fields of complex systems and systems engineering.     

“Neutron Shell”: a high efficiency array of neutron detectors for γ-ray spectroscopic studies with Gammasphere

A shell of neutron detectors was designed, constructed, and employed in γ-ray spectroscopy with Gammasphere. It consists of up to 35 tapered regular hexagons that replace the same number of forward Ge-detector modules in Gammasphere. The shell was designed for high detection efficiency and very good neutron–γ discrimination. The simultaneous use of time-of-flight, and two methods of pulse shape discrimination between neutrons and γ rays is described. Techniques for spectroscopy with efficient detection of two neutrons are discussed.     

Infons as mathematical objects

I argue that the role played by infons in the kind of mathematical theory of information being developed by several workers affiliated to CSLI is analogous to that of the various number systems in mathematics. In particular, I present a mathematical construction of infons in terms of representations and informational equivalences between them. The main theme of the paper arose from an electronic mail exchange with Pat Hayes of Xeroxparc. The exposition derives from a talk I gave at theTheories of Partial Information conference held at the University of Texas at Austin, January 1990.     

Current perspectives in residual ridge remodeling and its clinical implications: a review

PURPOSE: This article reviews the current understanding of the biology of tooth extraction wound healing and residual ridge remodeling. METHODS: The review of the biology of tooth extraction wound healing involves a discussion of the different cells populating the tooth extraction wound, the matrix formation, and the control of the repair process in the short-term. Defects in socket matrix formation or cellular activity will lead to stalled healing. The review of residual ridge remodeling describes the long-term result of tooth extraction and formation of residual ridges, in which the quantity of bone tissue continuously decreases. This may suggest that any potential regulatory factors of residual ridge resorption should have an adverse effect either on the increased catabolic activity by osteoclasts or on the decreased anabolic activity by osteoblasts. Both short-term tooth extraction healing and long-term residual ridge remodeling processes are interdependent. Furthermore, any potential genetic and environmental regulatory factors can affect the quality and quantity of bone by altering the gene expression events taking place in bone cells. RESULTS: The intent of this article was to review the current progresses of biologic research on residual ridge remodeling and to relate the changes at molecular, cellular, and tissue levels. The understanding of residual ridge remodeling may provide a sound scientific basis for improved restorative and therapeutic treatments of the edentulous population.     

Anticipated regret and organ donor registration—A pilot study.

Objective: In this pilot study we tested the influence of manipulating anticipated regret on organ donor registration behavior. Method: A simple web-based experimental trial was conducted. Nonorgan donors were allocated to a simple anticipated regret manipulation versus a theory of planned behavior or a control condition. The main outcome measure was registration on the U.K. organ donor register at 1-month follow-up. Results: A simple anticipated regret manipulation led to a significant increase in organ donor registrations. Conclusion: Interventions utilizing anticipated regret may have the potential to significantly increase organ donation rates. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

An efficient and effective similarity measure to enable data mining of petroglyphs

Rock art is an archaeological term for human-made markings on stone, including carved markings, known as petroglyphs, and painted markings, known as pictographs. It is believed that there are millions of petroglyphs in North America alone, and the study of this valued cultural resource has implications even beyond anthropology and history. Surprisingly, although image processing, information retrieval and data mining have had a large impact on many human endeavors, they have had essentially zero impact on the study of rock art. In this work we identify the reasons for this, and introduce a novel distance measure and algorithms which allow efficient and effective data mining of large collections of rock art.     

The “ick” factor, anticipated regret, and willingness to become an organ donor.

Objective: This research tested the role of traditional rational-cognitive factors and emotional barriers to posthumous organ donation. An example of an emotional barrier is the “ick” factor, a basic disgust reaction to the idea of organ donation. We also tested the potential role of manipulating anticipated regret to increase intention to donate in people who are not yet registered organ donors. Design: In three experiments involving 621 members of the United Kingdom general public, participants were invited to complete questionnaire measures tapping potential emotional affective attitude barriers such as the “ick” factor, the desire to retain bodily integrity after death, and medical mistrust. Registered posthumous organ donors were compared with nondonors. In Experiments 2 and 3, nondonors were then allocated to a simple anticipated regret manipulation versus a control condition, and the impact on intention to donate was tested. Main Outcome Measures: Self-reported emotional barriers and intention to donate in the future. Results: Traditional rational-cognitive factors such as knowledge, attitude, and subjective norm failed to distinguish donors from nondonors. However, in all three experiments, nondonors scored significantly higher than donors on the emotional “ick” factor and bodily integrity scales. A simple anticipated regret manipulation led to a significant increase in intention to register as an organ donor in future. Conclusions: Negative affective attitudes are thus crucial barriers to people registering as organ donors. A simple anticipated regret manipulation has the potential to significantly increase organ donation rates. (PsycINFO Database Record (c) 2011 APA, all rights reserved)