Exploiting Data Mining Techniques for Broadcasting Data in Mobile Computing Environments

Cataloged from PDF version of article.Mobile computers can be equipped with wireless communication devices that enable users to access data services from any location. In wireless communication, the server-to-client (downlink) communication bandwidth is much higher than the client-to-server (uplink) communication bandwidth. This asymmetry makes the dissemination of data to client machines a desirable approach. However, dissemination of data by broadcasting may induce high access latency in case the number of broadcast data items is large. In this paper, we propose two methods aiming to reduce client access latency of broadcast data. Our methods are based on analyzing the broadcast history (i.e., the chronological sequence of items that have been requested by clients) using data mining techniques. With the first method, the data items in the broadcast disk are organized in such a way that the items requested subsequently are placed close to each other. The second method focuses on improving the cache hit ratio to be able to decrease the access latency. It enables clients to prefetch the data from the broadcast disk based on the rules extracted from previous data request patterns. The proposed methods are implemented on a Web log to estimate their effectiveness. It is shown through performance experiments that the proposed rule-based methods are effective in improving the system performance in terms of the average latency as well as the cache hit ratio of mobile clients

Probing Charged Higgs Boson Couplings at the FCC-hh Collider

Many of the new physics models predicts a light Higgs boson similar to the Higgs boson of the Standard Model (SM) and also extra scalar bosons. Beyond the search channels for a SM Higgs boson, the future collider experiments will explore additional channels that are specific to extended Higgs sectors. We study the charged Higgs boson production within the framework of two Higgs doublet models (THDM) in the proton-proton collisions at the FCC-hh collider. With an integrated luminosity of 500 fb$^{-1}$ at very high energy frontier, we obtain a significant coverage of the parameter space and distinguish the charged Higgs-top-bottom interaction within the THDM or other new physics models with charged Higgs boson mass up to 1 TeV.Comment: 22 pages, 26 figures, 6 table

Processing count queries over event streams at multiple time granularities

Cataloged from PDF version of article.Management and analysis of streaming data has become crucial with its applications to web, sensor data, network traffic data, and stock market. Data streams consist of mostly numeric data but what is more interesting are the events derived from the numerical data that need to be monitored. The events obtained from streaming data form event streams. Event streams have similar properties to data streams, i.e., they are seen only once in a fixed order as a continuous stream. Events appearing in the event stream have time stamps associated with them at a certain time granularity, such as second, minute, or hour. One type of frequently asked queries over event streams are count queries, i.e., the frequency of an event occurrence over time. Count queries can be answered over event streams easily, however, users may ask queries over different time granularities as well. For example, a broker may ask how many times a stock increased in the same time frame, where the time frames specified could be an hour, day, or both. Such types of queries are challenging especially in the case of event streams where only a window of an event stream is available at a certain time instead of the whole stream. In this paper, we propose a technique for predicting the frequencies of event occurrences in event streams at multiple time granularities. The proposed approximation method efficiently estimates the count of events with a high accuracy in an event stream at any time granularity by examining the distance distributions of event occurrences. The proposed method has been implemented and tested on different real data sets including daily price changes in two different stock exchange markets. The obtained results show its effectiveness. (C) 2005 Elsevier Inc. All rights reserved

Concurrent rule execution in active databases

Cataloged from PDF version of article.An active DBMS is expected to support concurrent as well as sequential rule execution in an efficient manner. Nested transaction model is a suitable tool to implement rule execution as it can handle nested rule firing and concurrent rule execution well. In this paper, we describe a concurrent rule execution model based on parallel nested transactions. We discuss implementation details of how the flat transaction model of OpenOODB has been extended by using Solaris threads in order to SUppOrt COnCUrrent eXeCUtiOU of rUkS.

Automated construction of fuzzy event sets and its application to active databases

Fuzzy sets and fuzzy logic research aims to bridge the gap between the crisp world of math and the real world. Fuzzy set theory was applied to many different areas, from control to databases. Sometimes the number of events in an event-driven system may become very high and unmanageable. Therefore, it is very useful to organize the events into fuzzy event sets also introducing the benefits of the fuzzy set theory. All the events that have occurred in a system can be stored in event histories which contain precious hidden information. In this paper, we propose a method for automated construction of fuzzy event sets out of event histories via data mining techniques. The useful information hidden in the event history is extracted into a matrix called sequential proximity matrix. This matrix shows the proximities of events and it is used for fuzzy rule execution via similarity based event detection and construction of fuzzy event sets. Our application platform is active databases. We describe how fuzzy event sets can be exploited for similarity based event detection and fuzzy rule execution in active database systems

Dealing with fuzziness in active mobile database systems

Current needs of industry required the development of advanced database models like active mobile database systems. An active mobile database system can be designed by incorporation of triggering rules into a mobile computing environment in which the users are able to access a collection of database services using mobile and non-mobile computers at any location. Fuzzy concepts are adapted to the field of databases in order to deal with ambiguous, uncertain data. Fuzziness comes into picture in active mobile databases especially with spatial queries on moving objects. Incorporating fuzziness into rules would also improve the effectiveness of active mobile databases as it provides much flexibility in defining rules for the supported application. In this paper we present some methods to adapt the concepts developed for fuzzy systems to active mobile databases

Kilometer-long ordered nanophotonic devices by preform-to-fiber fabrication

Cataloged from PDF version of article.A preform-fo-fiber approach to the fabrication of functional fiber-based devices by thermal drawing in the viscous state is presented. A macroscopic preform rod containing metallic, semiconducting, and insulating constituents in a variety of geometries and close contact produces kilometer-long novel nanostructured fibers and fiber devices. We first review the material selection criteria and then describe metal-semiconductor-metal photosensitive and thermally sensitive fibers. These flexible, lightweight, and low-cost functional fibers may pave the way for new types of fiber sensors, such as thermal sensing fabrics, artificial skin, and large-area optoelectronic screens. Next, the preform-to-fiber approach is used to fabricate spectrally tunable photodetectors that integrate a photosensitive core and a nanostructured photonic crystal structure containing a resonant cavity. An integrated, self-monitoring optical-transmission waveguide is then described that incorporates optical transport and thermal monitoring. This fiber allows one to predict power-transmission failure, which is of paramount importance if high-power optical transmission lines are to be operated safely and reliably in medical, industrial and defense applications. A hybrid electron-photon fiber consisting of a hollow core (for optical transport by means of a photonic bandgap) and metallic wires (for electron transport) is described that may be used for transporting atoms and molecules by radiation pressure. Finally, a solid microstructured fiber fabricated with a highly nonlinear chalcogenide glass enables the generation of supercontinumn light at near-infrared wavelengths

International Migration, Remittances, and the Human Capital Formation of Egyptian Children

We study the roles that migration and remittances play in the human capital formation of children in Egypt. Our estimations reveal a significant association between remittances and human capital formation: the higher the probability of receipt of remittances, the higher the probability of school enrollment, and the older the age at which children enter the labor force. Although, with regard to the likelihood of school enrollment and the age of the first participation in the labor force, the family disruption effect of migration dominates the income effect of remittances, the likelihood of labor force participation decreases even in households from which both parents migrated

International Migration, Remittances, and the Human Capital Formation of Egyptian Children

We study the roles that migration and remittances play in the human capital formation of children in Egypt. Our estimations reveal a significant association between remittances and human capital formation: the higher the probability of receipt of remittances, the higher the probability of school enrollment, and the older the age at which children enter the labor force. Although, with regard to the likelihood of school enrollment and the age of the first participation in the labor force, the family disruption effect of migration dominates the income effect of remittances, the likelihood of labor force participation decreases even in households from which both parents migrated

Predicting Treatment Response in Social Anxiety Disorder From Functional Magnetic Resonance Imaging

Context: Current behavioral measures poorly predict treatment outcome in social anxiety disorder (SAD). To our knowledge, this is the first study to examine neuroimaging-based treatment prediction in SAD. Objective: To measure brain activation in patients with SAD as a biomarker to predict subsequent response to cognitive behavioral therapy (CBT). Design: Functional magnetic resonance imaging (fMRI) data were collected prior to CBT intervention. Changes in clinical status were regressed on brain responses and tested for selectivity for social stimuli. Setting: Patients were treated with protocol-based CBT at anxiety disorder programs at Boston University or Massachusetts General Hospital and underwent neuroimaging data collection at Massachusetts Institute of Technology. Patients: Thirty-nine medication-free patients meeting DSM-IV criteria for the generalized subtype of SAD. Interventions: Brain responses to angry vs neutral faces or emotional vs neutral scenes were examined with fMRI prior to initiation of CBT. Main Outcome Measures: Whole-brain regression analyses with differential fMRI responses for angry vs neutral faces and changes in Liebowitz Social Anxiety Scale score as the treatment outcome measure. Results: Pretreatment responses significantly predicted subsequent treatment outcome of patients selectively for social stimuli and particularly in regions of higher-order visual cortex. Combining the brain measures with information on clinical severity accounted for more than 40% of the variance in treatment response and substantially exceeded predictions based on clinical measures at baseline. Prediction success was unaffected by testing for potential confounding factors such as depression severity at baseline. Conclusions: The results suggest that brain imaging can provide biomarkers that substantially improve predictions for the success of cognitive behavioral interventions and more generally suggest that such biomarkers may offer evidence-based, personalized medicine approaches for optimally selecting among treatment options for a patient