Ascertaining Chronological Change Patterns in the Presence of Multiple Taxonomies

Data mining and knowledge discovery (KDD) is the technique of converting raw data into useful information. It is predictive technique for interesting data analysis. Change mining is technique of data mining that finds and reports changes in mined item set from one time to another time. Different data mining algorithms are evolved to show correlation among data mined. The data association changes from one time to time. The project highlights the HIGEN (HIGHLY GENERLISED PATTERN) algorithm that reports minimum level of abstraction of frequently generalized pattern. Association between items shown by algorithm for data coming from real time applications at multiple level of taxonomy. The experiment performed on artificial and factual datasets to show competence and effectiveness of proposed approach as well as usefulness of real time application context. DOI: 10.17762/ijritcc2321-8169.15010

A Study on Data Filtering Techniques for Event-Driven Failure Analysis

Engineering & Systems DesignHigh performance sensors and modern data logging technology with real-time telemetry facilitate system failure analysis in a very precise manner. Fault detection, isolation and identification in failure analysis are typical steps to analyze the root causes of failures. This systematic failure analysis provides not only useful clues to rectify the abnormal behaviors of a system, but also key information to redesign the current system for retrofit. The main barriers to effective failure analysis are: (i) the gathered sensor data logs, usually in the form of event logs containing massive datasets, are too large, and further (ii) noise and redundant information in the gathered sensor data that make precise analysis difficult. Therefore, the objective of this thesis is to develop an event-driven failure analysis method in order to take into account both functional interactions between subsystems and diverse user???s behaviors. To do this, we first apply various data filtering techniques to data cleaning and reduction, and then convert the filtered data into a new format of event sequence information (called ???eventization???). Four eventization strategies: equal-width binning, entropy, domain knowledge expert, and probability distribution estimation, are examined for data filtering, in order to extract only important information from the raw sensor data while minimizing information loss. By numerical simulation, we identify the optimal values of eventization parameters. Finally, the event sequence information containing the time gap between event occurrences is decoded to investigate the correlation between specific event sequence patterns and various system failures. These extracted patterns are stored in a failure pattern library, and then this pattern library is used as the main reference source to predict failures in real-time during the failure prognosis phase. The efficiency of the developed procedure is examined with a terminal box data log of marine diesel engines.ope