Smart Information Management for Smarter Decision Making
Abstract
In this Internet world, Data is the most valuable resource of an enterprise. In this competitive world, it is a tedious process to make business decisions when using the large database. The retrieval of information is difficult and time consuming. Also it is the responsibility of the user to allocate enough memory to store the information.
To search for an item, it takes the framework for a particular user to extract the information from the server. With this tremendous growth of network services, the problem rate also gets increased. To overcome this problem, suitable techniques are applied in this article.
The information is extracted by sending the request to the server and waits for the response. This method of request/reply is better only for some time. In some situation, when the lack of users are sending the sending the request to the server, the burden of the server gets increased and it automatically goes down. So, to reduce the server burden, we need a special technique that is also reliable to the user.
In this paper, we propose the Disconnected Architecture, which is user-friendly to access and extract the information from the database. This Architecture maintains a temporary memory to store the items which are frequently accessed by the user. This temporary memory is called as Log. With the help of this log, the server burden is reduced and the performance of the server gets raised.
When the server receives the request, it search the log whether this kind of request is already processed by it and if so, it sends back the same reply as before. Otherwise, it processes the new request and sends the reply to the user.
In this article, we can extract the information from the database using the Mining Techniques. This paper also describes the practicalities and the constraints in Data Mining and its Advancements.
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“Tools for Privacy Preserving Distributed Data Mining” by Chris Clifton, Murat Kantarcioglu, Jaideep Vaidya and Xiaodong Lin, Michael Y.Zhu, Prude University.
A. Eisenberg, With false numbers, data crunchers try to mine the truth. New York Times, July 18,2002.
M. Hamblen. Privacy algorithms: Technology-based protections could make personal data impersonal. Computer World. Oct 14, 2002.
S. Evfimievski. Randomization techniques for privacy-preserving association rule mining. SIGKDD Explorations, 4(2). Dec 2002.
“Frequent Itemset Mining on Graphics Processors” by Wenbin Fang, Mian Lu, Xiangye Xiao, Bingsheng He, Qiong Luo, Hong Kong University of Science and Technology.
Rakesh Agrawal, Tomasz Imieli¶nski, and Arun Swami.Mining association rules between sets of items in large databases. SIGMOD, 1993.
Rakesh Agrawal and Ramakrishnan Srikant. Fast algorithms for mining association rules. VLDB, 1994.
Jiawei Han, Jian Pei, Yiwen Yin, and Runying Mao. Mining frequent patterns without candidate generation: A frequent-pattern tree approach. Data Mining and Knowledge Discovery, 2004.
Lamine M. Aouad, Nhien-An Le-Khac, and Tahar M.Kechadi. Distributed frequent itemsets mining in heterogeneous platforms. Journal of Engineering, Computing and Architecture, 2007.
Gregory Buehrer, Srinivasan Parthasarathy, Shirish Tatikonda, Tahsin Kurc, and Joel Saltz. Toward terabyte pattern mining: an architecture-conscious solution. PPoPP, 2007.
Mohammad El-Hajj and Osmar R. Zaiane. Parallel leap: Large-scale maximal pattern mining in a distributed environment. ICPADS, 2006.
Haoyuan Li, Yi Wang, Dong Zhang, Ming Zhang, and Edward Y. Chang. Pfp: Parallel fp-growth for query recommendation. ACM Recommender Systems, 2008.
Amol Ghoting, Gregory Buehrer, Srinivasan Parthasarathy, Daehyun Kim, Anthony Nguyen, Yen- Kuang Chen, and Pradeep Dubey. Cache-conscious frequent pattern mining on a modern processor. VLDB, 2005.
Naga K. Govindaraju, Brandon Lloyd, Wei Wang, Ming Lin, and Dinesh Manocha. Fast computation of database operations using graphics processors. SIGMOD, 2004.
Li Liu, Eric Li, Yimin Zhang, and Zhizhong Tang. Optimization of frequent itemset mining on multiple-core processor. VLDB, 2007.
Yanbin Ye and Chia-Chu Chiang. A parallel apriori algorithm for frequent itemsets mining. SERA, 2006.
Ferenc Bodon. A fast apriori implementation. FIMI, 2003.
Christian Bienia, Sanjeev Kumar, Jaswinder Pal Singh, and Kai Li. The parsec benchmark suite: Characterization and architectural implications. PACT, 2008.
Jayaprakash Pisharath, Ying Liu, Wei keng Liao, Alok Choudhary, Gokhan Memik, and Janaki Parhi. Nu-minebench 2.0. Technical report, Northwestern University, 2005.
“Grid Based Distributed Data Mining Systems, Algorithms and Services” by Domenico Talia.
“Distributed Frequent Itemset Mining using Trie Data Structure” by E.Ansari, G.H. Dastghaibifard, M. Keshtkaran, H. Kaabi.
“Parallel and Distributed Frequent Itemset Mining on Dynamic Datasets” by Adriano Veloso, Matthew Erick Otey, Srinivasan Parthasarathy, and Wagner Meira.
“Privacy Preserving Frequent Itemset Mining” by Stanley R.M. Oliveria, Osmar R.Zaiane , University of Cananda and Alberta.
“An Audit Environment for Outsourcing of Frequent Item set Mining” by W.K. Wong, David W.Cheung, Edward Hung, Ben Kao and Nikos Mamoulis.
Rakesh Agrawal, Tomasz Imielinski, and Arun Swami, Database Mining: A Performance Perspective", IEEE Transactions on Knowledge and Data Engineering, Special Issue on Learning and Discovery in Knowledge-Based Databases, (to appear).
Rakesh Agrawal, Sakti Ghosh, Tomasz Imielinski, Bala Iyer, and Arun Swami, An Interval Classifier for Database Mining Applications", VLDB-92, Vancouver, British Columbia, 1992, 560{573.
Dina Bitton, Bridging the Gap Between Database Theory and Practice", Cadre Technologies, Menlo Park, 1992.
L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone, Classification and Regression Trees, Wadsworth, Belmont, 1984.
B. Falkenhainer and R. Michalski, Integrating Quantitative and Qualitative Discovery: The ABACUS System", Machine Learning, 1(4): 367{ 401.
M. Kokar, Discovering Functional Formulas through Changing Representation Base", Proceed- ings of the Fifth National Conference on Artificial Intelligence, 1986, 455{459.
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