Dissemination of Dynamic Data: Semantics, Algorithms, and Performance

(Extended Abstract)
  • Krithi Ramamritham
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3882)


The Internet and the Web are increasingly used to disseminate fast changing data such as sensor data, traffic and weather information, stock prices, sports scores, and even health monitoring information. These data items are highly dynamic, i.e., the data changes continuously and rapidly, streamed in real-time, i.e., new data can be viewed as being appended to the old or historical data, and aperiodic, i.e., the time between the updates and the value of the updates are not known a priori. Increasingly, users are interested in monitoring such data for online decision making. To provide users with dynamic, interactive, and personalized experiences, websites are relying on dynamic content generation applications, which build Web pages on the fly based on the run-time state of the website and the user session on the site. These applications make use of database backends. But, these benefits come at a cost, each request for a dynamic page requires computation as well as communication across multiple components inside the data dissemination and information processing infrastructure.

Consider the following scenario.

A company involved in developing IT enabled services responds to Request For proposals (RFPs). Often RFPs are brought to its attention by customers, sometimes through word of mouth. Won’t it be convenient if the posting of a relevant RFP at a (potential) customer’s website is automatically brought to the attention of the appropriate business unit or group within company? Our work is motivated by such needs – the need to constantly track and monitor the dynamics of information sources – some of which are identified through historical access patterns, others by monitoring potentially useful sites judiciously. Also, often a company responding to RFPs is looking to bolster its case by citing completed projects where the relevant skillsets have been demonstrated. The needed information can be retrieved by maintaining a knowledge repository and setting the following query that continuously sends up-to-date information, as the knowledge base gets updated, to the proposal writer(s).


Dynamic Data Continuous Query User Session Computation Overhead Knowledge Repository 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Krithi Ramamritham
    • 1
  1. 1.Indian Institute of TechnologyBombayIndia

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