Efficient storage and querying of sequential patterns in database systems
Presented By:
Alexandros Nanopoulosa, , Maciej Zakrzewiczb, , Tadeusz Morzyb, and Yannis Manolopoulos

Abstract
The number of patterns discovered by data mining can become tremendous, in some cases exceeding the size of the original database. Therefore, there is a requirement for querying previously generated mining results or for querying the database against discovered patters. In this paper, we focus on developing methods for the storage and querying of large ....etc

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Privacy-Conscious Location-Based Queries in
Mobile Environments



ABSTRACT:

In location-based services, users with location-aware mobile devices are able to make queries about their surroundings anywhere and at any time. While this ubiquitous computing paradigm brings great convenience for information access, it also raises concerns over potential intrusion into user location privacy. To protect location privacy, one typical approach is to cloak user locations into spatial regions based on user-specified p ....etc

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C-TREND: Temporal Cluster Graphs for Identifying and Visualizing Trends in Multi-attribute Transactional Data

Organizations and firms are capturing increasingly more data about their customers, suppliers, competitors, and business environment. Most of this data is multi-attribute (multidimensional) and temporal in nature. Data mining and business intelligence techniques are often used to discover patterns in such data; however, mining temporal relationships typically is a complex task. We propose a new data analysis and visualization technique ....etc

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there are new patterns of disease and pest attack with changes in rainfall patterns

As climate change alters environmental conditions, the incidence and global patterns of human diseases are changing. These modifications to disease profiles and the effects upon human pharmaceutical usage are discussed. Climate-related environmental changes are associated with a rise in the incidence of chronic diseases already prevalent in the Northern Hemisphere, for example, cardiovascular disease and mental illness, leading to greater use of associated heav ....etc

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A Framework for Mining Sequential Patterns from Spatio-Temporal Event Data Sets-IEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 20, NO. 4,-java

Abstract +
Given a large spatio-temporal database of events, where each event consists of the fields event ID, time, location, and event type, mining spatio-temporal sequential patterns identifies significant event-type sequences. Such spatio-temporal sequential patterns are crucial to the investigation of spatial and temporal evolutions of phenomena in many application domains. Rec ....etc

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To get full information or details of a signature based indexing method retrieval of relative temporal patterns please have a look on the pages

http://seminarsprojects.net/Thread-a-signature-based-indexing-method-for-efficient-content-based-retrieval-of-relative-t--8605?pid=12455

http://seminarsprojects.net/Thread-a-signature-based-indexing-method-for-efficient-content-based-retrieval-of-relative-t

if you again feel trouble on a signature based indexing method retrieval of relative temporal patterns please reply in that page and ask specific ....etc

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LOCATION BASED SPATIAL QUERY PROCESSING IN WIRELESS BROADCAST ENVIRONMENTS-MOBILE COMPUTING

Abstract: Location-based spatial queries (LBSQ s) refer to spatial queries whose answers rely on the location of the inquirer. Efficient processing of LBSQ s is of critical importance with the ever-increasing deployment and use of mobile technologies. We show that LBSQ s has certain unique characteristics that the traditional spatial query processing in centralized databases does not address. For example, a significant challenge is presented by wirel ....etc

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C-TREND:Temporal Cluster Graphs for Identifying and Visualizing Trends in Multi-attribute Transactional Data

Organizations and firms are capturing increasingly more data about their customers, suppliers, competitors, and business environment. Most of this data is multi-attribute (multidimensional) and temporal in nature. Data mining and business intelligence techniques are often used to discover patterns in such data; however, mining temporal relationships typically is a complex task. We propose a new data analysis and visualization techniqu ....etc

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to get information about the topic C-TREND:Temporal Cluster Graphs for Identifying and Visualizing Trends full report ,ppt and related topic refer the page link bellow

http://seminarsprojects.net/Thread-c-trend-temporal-cluster-graphs-for-identifying-and-visualizing-trends

http://seminarsprojects.net/Thread-c-trend-temporal-cluster-graphs-for-identifying-and-visualizing-trends-in-multiattri

http://seminarsprojects.net/Thread-c-trend-temporal-cluster-graphs-for-identifying-and-visualizing-trends?pid=63598#pid63598 ....etc

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please go through the following threads to get more information on 'Location based Spatial Query Processing in Wireless Environments'

http://seminarsprojects.net/Thread-location-based-spatial-query-processing-in-wireless-broadcast-environments
http://seminarsprojects.net/Thread-location-based-spatial-query-processing-in-wireless-broadcast-environments-mobile-com ....etc

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