Pattern Evaluation Proved To Be Important For Decision Support in Data Warehouse
Trivedi Prakashkumar Hitendrabhai · IOSR Journal of Engineering · 2012
It is the mobile sensor network or satellite that is observing and obtaining the data pattern.The study focuses on spatio-temporal data and events pattern for the data mining and for data warehouse.There is a repository or database where sensor, satellite inputs or data are collected for analysis.The depiction of satellite or mobile sensor and network leads new path for patterns, data mining and data warehouse.From the remote, the data obtained by using the mobile sensor, satellite so that data mining technique perform for finding desirable patterns.The study introduces moving point object data and algorithm as an advance research.The existing concept of moving point object based flock and leadership algorithm is exercising with uninterrupted data movement.It instructs that data moving in interrupted state.Present study focuses on an implementation of moving point object based Flock and Leadership algorithm.The algorithm is functioning with moving point object data but interrupted condition.The interrupted data produced abnormal result because the input data is captured in interrupted state.From the inputs the study observes or collects spatial, temporal based elements or properties.There will be Spatial Temporal an event pattern.After obtaining data by using mobile sensor network or satellite network.The patterns are essential and useful which is desirable.The study instructing that patterns evaluation proved to be important for decision support in data warehouse.Further the study focuses on significance of pattern and its evaluation.There are events like earthquake, flood, desert events that are observed or obtained as an input of the work.They were abnormal in input stage but the data mining process has made data proper and obtained proper resulting data.On the basis of the selected pattern or data there can be a decision.The decision can be forecasting for earthquake, flood, or desert events.Finally the study observes the performance of pattern in all the stages of the study and has obtained the result of its importance and performance of the pattern for the decision support system in data warehouse.