摘要 :
Data sciences have been commercially adopted by various industries as a new arising era of technology but in the field of research still a lot more can be achieved. Data in data warehouse generally reflects the historical data tha...
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Data sciences have been commercially adopted by various industries as a new arising era of technology but in the field of research still a lot more can be achieved. Data in data warehouse generally reflects the historical data that is separated from the operational data of the industry. The exploration of data is done by the online analytical processing (OLAP). OLAP from its initialization has been a subject of various modifications. It has been reviewed and imposed under various conditions to test its working. Majorly it has been used with various kinds of datasets such, spatial, traditional, sequential, high-dimensional etc. Research now is not only confined to any particular data source in fact a wide spectrum of data sources are to be considered. Apart from analysing the data, visualizing also is an important aspect to be considered, as perception creates a more plausible impression. This paper discusses various existing methodologies of data analysis. It also refers to the frequently used visualization techniques and the various commercial BI tools that are successful and the methods that these tools work on. It also aims towards providing an interface that would analyse, link and visualize the data in the same frame.
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摘要 :
Data sciences have been commercially adopted by various industries as a new arising era of technology but in the field of research still a lot more can be achieved. Data in data warehouse generally reflects the historical data tha...
展开
Data sciences have been commercially adopted by various industries as a new arising era of technology but in the field of research still a lot more can be achieved. Data in data warehouse generally reflects the historical data that is separated from the operational data of the industry. The exploration of data is done by the online analytical processing (OLAP). OLAP from its initialization has been a subject of various modifications. It has been reviewed and imposed under various conditions to test its working. Majorly it has been used with various kinds of datasets such, spatial, traditional, sequential, high-dimensional etc. Research now is not only confined to any particular data source in fact a wide spectrum of data sources are to be considered. Apart from analysing the data, visualizing also is an important aspect to be considered, as perception creates a more plausible impression. This paper discusses various existing methodologies of data analysis. It also refers to the frequently used visualization techniques and the various commercial BI tools that are successful and the methods that these tools work on. It also aims towards providing an interface that would analyse, link and visualize the data in the same frame.
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摘要 :
Data sciences have been commercially adopted by various industries as a new arising era of technology but in the field of research still a lot more can be achieved. Data in data warehouse generally reflects the historical data tha...
展开
Data sciences have been commercially adopted by various industries as a new arising era of technology but in the field of research still a lot more can be achieved. Data in data warehouse generally reflects the historical data that is separated from the operational data of the industry. The exploration of data is done by the online analytical processing (OLAP). OLAP from its initialization has been a subject of various modifications. It has been reviewed and imposed under various conditions to test its working. Majorly it has been used with various kinds of datasets such, spatial, traditional, sequential, high-dimensional etc. Research now is not only confined to any particular data source in fact a wide spectrum of data sources are to be considered. Apart from analysing the data, visualizing also is an important aspect to be considered, as perception creates a more plausible impression. This paper discusses various existing methodologies of data analysis. It also refers to the frequently used visualization techniques and the various commercial BI tools that are successful and the methods that these tools work on. It also aims towards providing an interface that would analyse, link and visualize the data in the same frame.
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摘要 :
In this paper we present a more effective method to discover the periodicity in web log sequence data which handle missing sequences which may occur during the aggregation process, such as sequences that swing between two periods....
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In this paper we present a more effective method to discover the periodicity in web log sequence data which handle missing sequences which may occur during the aggregation process, such as sequences that swing between two periods. On other hands, a sequence may start near the end time of a period where the rest of those sequences appear in next period however,these kinds of issues certainly it will leave its effect of periodicity discovery. Moreover, we incorporated OLAP data cube techniques in the aggregation process in order to handle large generated sequences and visualised the discovered periodic patterns, in order to study, its impact on periodicity discovery.
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