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

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.

Evaluation metrics for measuring bias in search engine results

Search engines decide what we see for a given search query. Since many people are exposed to information through search engines, it is fair to expect that search engines are neutral. However, search engine results do not necessarily cover all the viewpoints of a search query topic, and they can be biased towards a specific view since search engine results are returned based on relevance, which is calculated using many features and sophisticated algorithms where search neutrality is not necessarily the focal point. Therefore, it is important to evaluate the search engine results with respect to bias. In this work we propose novel web search bias evaluation measures which take into account the rank and relevance. We also propose a framework to evaluate web search bias using the proposed measures and test our framework on two popular search engines based on 57 controversial query topics such as abortion, medical marijuana, and gay marriage. We measure the stance bias (in support or against), as well as the ideological bias (conservative or liberal). We observe that the stance does not necessarily correlate with the ideological leaning, e.g. a positive stance on abortion indicates a liberal leaning but a positive stance on Cuba embargo indicates a conservative leaning. Our experiments show that neither of the search engines suffers from stance bias. However, both search engines suffer from ideological bias, both favouring one ideological leaning to the other, which is more significant from the perspective of polarisation in our society

Enamel Factors Regulate Expression of Genes Associated With Cementoblasts

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141313/1/jper1829.pd

Towards trajectory anonymization: A generalization-based approach

Trajectory datasets are becoming,popular,due,to the massive,usage,of GPS and,location- based services. In this paper, we address privacy issues regarding the identification of individuals in static trajectory datasets. We first adopt the notion of k-anonymity,to trajectories and propose,a novel generalization-based approach,for anonymization,of trajectories. We further show,that releasing anonymized,trajectories may,still have,some,privacy,leaks. Therefore we propose,a randomization based,reconstruction,algorithm,for releasing anonymized,trajectory data and,also present how,the underlying,techniques,can be adapted,to other anonymity,standards. The experimental,results on real and,synthetic trajectory datasets show,the effectiveness of the proposed,techniques

Foreword for the special issue of selected papers from the 3rd ACM SIGSPATIAL Workshop on Security and Privacy in GIS and LBS

The third Workshop on Security and Privacy in GIS and LBS (SPRINGL 2010) was organized in November 2, 2010, San Jose, California in conjunction with the SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM GIS 2010). Security and privacy are the two dimensions of GIS systems and geospatial applications that need to be addressed for these applications to have wider acceptance. However, we are still far from fully achieving this goal with provable techniques that can be adopted by the industry. The SPRINGL workshop series aims to provide a forum for researchers working in the field of geospatial data security and privacy to discuss the advances in this domain. In order for solid archival work to be presented to the community, special issues of Transactions on Data Privacy have been organized for the previous SPRINGL workshops. This special issue contains three extended papers that have been selected from the papers presented at SPRINGL 2010 focusing mainly on the privacy aspects

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

Foreword for the special issue of selected papers from the 3rd ACM SIGSPATIAL Workshop on Security and Privacy in GIS and LBS

The third Workshop on Security and Privacy in GIS and LBS (SPRINGL 2010) was organized in November 2, 2010, San Jose, California in conjunction with the SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM GIS 2010). Security and privacy are the two dimensions of GIS systems and geospatial applications that need to be addressed for these applications to have wider acceptance. However, we are still far from fully achieving this goal with provable techniques that can be adopted by the industry. The SPRINGL workshop series aims to provide a forum for researchers working in the field of geospatial data security and privacy to discuss the advances in this domain. In order for solid archival work to be presented to the community, special issues of Transactions on Data Privacy have been organized for the previous SPRINGL workshops. This special issue contains three extended papers that have been selected from the papers presented at SPRINGL 2010 focusing mainly on the privacy aspects