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Emmanuel, M.
- Review and Comparative Study of Bitmap Indexing Techniques
Abstract Views :171 |
PDF Views:2
Authors
Sagar S. Mane
1,
M. Emmanuel
2
Affiliations
1 Pune Institute of Computer Technology, Pune, Maharashtra, IN
2 Information Technology Department, Pune Institute of Computer Technology, Pune, Maharashtra, IN
1 Pune Institute of Computer Technology, Pune, Maharashtra, IN
2 Information Technology Department, Pune Institute of Computer Technology, Pune, Maharashtra, IN
Source
Data Mining and Knowledge Engineering, Vol 7, No 1 (2015), Pagination: 30-34Abstract
Decision support systems that access data from large databases are mainly designed to handle complex and ad hoc queries. Now a days, the massive, non-volatile, and subject-oriented databases can include the processing of analytical and interactive queries that need quick response time with high accuracy. To enhance the data mining queries performance, many techniques such as various types of indices, materialized views and data fragmentation are used. The bitmap indices are mostly suitable in read mostly datasets like data warehouses and transactional databases. The main benefit of using bitmap indices is that bitmap vectors can be directly accessed without decompression and it helps to improve processing time for complex and interactive queries. They significantly use low cost Boolean operations and check predicate conditions on the index level prior to accessing to the primary source data. This paper presents bitmap indexing techniques for data warehouses along with their analysis and comparison among them.Keywords
Iceberg Query, Bitmap Index, Data Warehouse, Data Mining, Bitwise-AND Operation.
- Optimizing Business Processes Using Process Mining Techniques
Abstract Views :174 |
PDF Views:2
Authors
Affiliations
1 Pune Institute of Computer Technology, Pune, Maharashtra, IN
1 Pune Institute of Computer Technology, Pune, Maharashtra, IN
Source
Data Mining and Knowledge Engineering, Vol 7, No 1 (2015), Pagination: 39-41Abstract
Organizations use business process modelling software to develop their functioning systems. The business process management software has helped to integrate the various modules of an organization in order to complete task/tasks. More recently, the work for providing an optimum solution is underway. Process mining techniques can be used to monitor the software development process. Business processes leave their footprints in event logs and recent research in process mining make it possible to discover and optimize business processes based on the analysis of such logs. These logs can be used for knowledge mining and hence can be used to provide an optimal solution regarding the generation of the process.Keywords
Business Process Management, Conformance Checking, Task Operation Model.- Emotion Recognition from Text-A Survey
Abstract Views :178 |
PDF Views:2
Authors
Affiliations
1 Pune Institute of Computer Technology, IN
2 Department of Information Technology, Pune University of Computer Technology, IN
1 Pune Institute of Computer Technology, IN
2 Department of Information Technology, Pune University of Computer Technology, IN
Source
Data Mining and Knowledge Engineering, Vol 6, No 3 (2014), Pagination: 113-116Abstract
Emotion is a very important facet of human behaviour which affect on the way people interact in the society. In recent year many methods on human emotions recognition have been published such as recognizing emotion from facial expression and gestures, speech and by written text. This paper focuses on classification of emotion expressed by the online text, based on pre-defined list of emotion. The collection of dataset is the basic step, which is collected from the various sources like daily used sentences, user status from various social networking websites such as facebook and twitter. Using this data set we target only on the keywords that show human emotions. The targeted keywords are extracted from the dataset and translated into the format which can be processed by the classifier to finally generate the Predicting model which is further compared by the test dataset to give the emotions in the input sentences or documents.Keywords
Affective Computing, Classification, Document Categorization, Emotion Detections.- Enhancing HiveQL Engine Using Map-Join-Reduce
Abstract Views :150 |
PDF Views:3
This HiveQL is allowing enhancement of MapReduce to MapJoinReduce for our convenience. This will lead us for detailed study of performance improvement.
The programmer is only required to write specialized map and reduce functions as part of the Map/Reduce job. Framework takes care of the rest. But MapReduce finds performance issue. The performance issue is mainly due to MapReduce sequential data processing strategy which frequently checkpoints and shuffles intermediate results in data processing. So MapReduce can be improved to increase scalability and efficiency.
And proposed solution is Map-Join-Reduce. Map-Join-Reduce remove the burden of presenting complex join algorithms to the system. We first proposed filter-join-aggregate mathematical model which is an extension of MapReduce model. To support this mathematical model we present a MapJoinReduce architecture design for HiveQL engine. This architecture design will put light on strategy of query processing by Hive system and Hadoop system.
Benefit of this approach is minimized check pointing and shuffling of intermediate result and further more improves performance of system.
Authors
Affiliations
1 Pune Institute of Computer Technology College, Pune, Maharashtra, IN
2 Department of Information Technology, PICT, Pune, IN
3 PICT College of Engineering, Pune, IN
1 Pune Institute of Computer Technology College, Pune, Maharashtra, IN
2 Department of Information Technology, PICT, Pune, IN
3 PICT College of Engineering, Pune, IN
Source
Data Mining and Knowledge Engineering, Vol 5, No 1 (2013), Pagination: 9-12Abstract
Hive is a data warehouse system for Hadoop that facilitates easy data summarization, ad-hoc queries, and the analysis of large datasets stored in Hadoop compatible file systems. Hive provides a mechanism to project structure onto this data and query the data using a SQL-like language called HiveQL. At the same time this language also allows traditional map/reduce programmers to plug in their custom mappers and reducers when it is inconvenient or inefficient to express this logic in HiveQL.This HiveQL is allowing enhancement of MapReduce to MapJoinReduce for our convenience. This will lead us for detailed study of performance improvement.
The programmer is only required to write specialized map and reduce functions as part of the Map/Reduce job. Framework takes care of the rest. But MapReduce finds performance issue. The performance issue is mainly due to MapReduce sequential data processing strategy which frequently checkpoints and shuffles intermediate results in data processing. So MapReduce can be improved to increase scalability and efficiency.
And proposed solution is Map-Join-Reduce. Map-Join-Reduce remove the burden of presenting complex join algorithms to the system. We first proposed filter-join-aggregate mathematical model which is an extension of MapReduce model. To support this mathematical model we present a MapJoinReduce architecture design for HiveQL engine. This architecture design will put light on strategy of query processing by Hive system and Hadoop system.
Benefit of this approach is minimized check pointing and shuffling of intermediate result and further more improves performance of system.