Project type: Application, web/windows based
Project title: Decision Support System for a Fuel Management company
Background/Introduction
Energy Care Ltd (ECL) is a company started with the objective of providing Fuel Management Solution to industrial consumers like power plants, cement plants and fertilizer plants etc within the country. At present they are supplying coal to these organizations. Their overseas business associate EC Worldwide Inc(ECW) helps ECL in sourcing coal from mines located in Australia, South Afr ....etc

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Spatial Data Query Support in Peer-to-Peer Systems



Motivation

Spatial data sets are used for many applications, e.g., GIS, CAD,
P2P systems provide a distributed platform that is very scalable.
Pros:
Scalability, no central point of failure
Cons:
Very dynamic (unreliable), topology maintenance required

Distributed Hash Tables (DHT)

DHT systems: Content Addressable Network (CAN), Chord, Pastry, etc.
Using DHT to allocate large data sets to many nodes with no ....etc

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PRESTO Feedback-Driven Data Management in Sensor Networks

Abstract:
This paper presents PRESTO, a novel two-tier sensor data management architecture comprising proxies and sensors that cooperate with one another for acquiring data and processing queries. PRESTO proxies construct time-series models of observed trends in the sensor data and transmit the parameters of the model to sensors. Sensors check sensed data with model-predicted values and transmit only deviations from the predictions back to t ....etc

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Data Warehouses, Decision Support and Data Mining

A Data Warehouse is a subject-oriented,integrated,time-variant,non-volatile collection of data used in support of management decision making processes.

Subject-Oriented:-
The data warehouse is organized around the key subjects (or high-level entities) of the enterprise.

Major subjects include
a) Customers
b) Patients
c) Students
d) Products
e) Etc.

Data support :-

Stored collection of diverse data
A solution to data integration problem
Single repository of information
Sub ....etc

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I am kannan .i am studing mechanical engi.i have a seminar presentation on next week.i selected few topics from this site. I want a detail report on this topics ....etc

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Decision Support System for Managing Educational Capacity Utilization

Abstract

Decision-making in the field of academic planning involves extensive analysis of large data volumes originating from multiple systems.With the many new technology application areas evolving from the domain of electrical engineering, computer engineering, and computer science, deans and department chairs must ensure that new specializations and programs are adequately supported. Academic workload management is concerned with di ....etc

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Abstract

Traditional decision tree classifiers work with data whose values are known and precise. We extend such classifiers to handle data with uncertain information. Value uncertainty arises in many applications during the data collection process. Example sources of uncertainty include measurement/quantization errors, data staleness, and multiple repeated measurements. With uncertainty, the value of a data item is often represented not by one single value, but by multiple values forming a probability distribution. Rather than abstracting unc ....etc

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what are the distributed based algorithms used in the decision ....etc

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Abstract
Efficiency and scalability are fundamental issues concerning data mining in large databases. Although classification has been studied extensively, few of the known methods take serious consideration of efficient induction in large databases and the analysis of data at multiple abstraction levels. The paper addresses the efficiency and scalability issues by proposing a data classification method which integrates attribute oriented induction, relevance analysis, and the induction of decision trees. Such an integration leads to efficient ....etc

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Classification is a classical problem in machine learning
and data mining. Given a set of training data tuples, each
having a class label and being represented by a feature vector,
the task is to algorithmically build a model that predicts
the class label of an unseen test tuple based on the tuple s
feature vector. One of the most popular classification models
is the decision tree model. Decisions trees are popular because
they are practical and easy to understand. Rules can also be
extracted from decision trees easily. Many algorithms ....etc

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