Actually I need these for the data generated from mobile (moving) platforms hence I wrote sensor devices.
several major kinds of data mining methods can be applied including generalization, characterization, classification, clustering, association, evolution, pattern matching, data visualization and meta-rule guided mining. you should first construct the database and apply the methods with WEKA or other tools implemented in the DBMS.
I appreciate your prompt reply. I may use the same on Hadoop, do you have any experience for this use case by using MapReduce Architecture.
I have experience in data mining and distributed system (parallel programming) . can you explain more the application may i can help you? Have you a big data base or you need to reduce the time of a real complex and big algorithms?
No, I want to make analysis of the data generated from the sensor devices(the devices which are moving) so i make take RFID data, GPS data etc. So by using MapReduce algorithms i want to do it. But for that I was thinking Data mining algorithms which you listed and I had thought about classification , clustering and pattern matching which may be converted into MapReduce algorithms . I hope, it must have been cleared to you. Thanks
Traditional Mining techniques are more suited for offline processing. However, Sensor devices are more used in the business context of real-time results.
What is your use-case? If your goal is reporting and aggregating, then sensor devices can be treated as just another data-source for offline processing. Nothing much different. In case your problem is more of real-time decision making (such as geo-location tracking and eco-routing) then map-reduce cannot satisfy the real-time requirements. You would have to go for CEP engines with stream-processing architectures.
Thank you,
GK (http://gk.palem.in/)
What is your suggestion? For using Map Reduce what should I do? How to make use of CEP engines? Thanks in advance.
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