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Phd Thesis In Data Warehousing - …

10th:89% – CBSE
12th:97% – State Board ( Got general proficiency award )
GPA: 8.2/10
GRE: 307 ; AWA: 3.5
TOEFL: 103
Received merit scholarship during my First year Undergrad. I have published 2 IEEE papers, followed by a national level presentation at a Texas Competition and 1 ARPN journal( its in my EEE field) ; My Final year project was selected for full funding by the College.
Working since last 1.5 Yrs in a Leading IT company in the Data Warehouse-Business Intelligence Domain.
Additionally i have other co-curriculars as to being organizer for various events, 2 in-plant trainings and am a classical dancer for 15 yrs having given many performances in India and abroad.

Data Warehousing Research Papers Thesis

This batch file processing framework is very efficient for periodic data acquisition into a data warehouse. The overhead of starting up a batch job is easily outweighed by the superior efficiency of sequential file processing with large volumes of data. However, in a real-time data warehousing implementation, it is no longer acceptable to accumulate the large volumes of data necessary to make file processing an efficient method of data acquisition. It is clearly unacceptable to wait until the end of the day (or week) to load data into a real-time data warehouse with extreme service levels for data freshness. Since real-time data warehousing means frequent acquisition of data into the warehouse, there will not be sufficient volume accumulated in any given load cycle to merit batch data processing implementations. If the number of rows being loaded into a data warehouse is less than the number of blocks in the target table, it is typically more efficient to use continuous data acquisition with SQL inserts than bulk data loading (it also depends on the number of indices on the table and will vary by RDBMS product).

Data mining and data warehousing phd thesis - …

In addition, we describe the modeling options for the backorder processing for replenishment orders sent to the warehouse.

Mistake 3: Using Legacy ETL Infrastructure
Traditional data warehouses have been built with batch file processing as the foundation for data acquisition. Homegrown and commercially available ETL (extract, transform, and load) tools have been built around a model of data-level integration using non-intrusive extracts from the operational bookkeeping systems with meta data used to define source-to-target mappings using code generators or engine-based transformation tools. The execution framework for these tools is to take “raw” source files as input and then use the meta data to drive transformation of the source files into outputs that are load ready for insert, update, and delete operations into the data warehouse.

Real-time data warehousing has two very important implications for the ETL infrastructure: (1) data delivery needs to be more explicitly supported from the operational source systems, and (2) data transformation implementations must evolve from a file processing orientation to a stream processing orientation. Legacy ETL infrastructures are primarily non-intrusive. Very little involvement from the source systems is required because the ETL processes extract from file structures using back-door processes without requiring changes to legacy applications. However, to facilitate real-time data warehousing, the operational bookkeeping applications must be enhanced to explicitly publish data to EAI message bus structures. This will require changes to the legacy applications to interface with the EAI infrastructure within the enterprise.

Thesis Data Warehousing Phd - …

Real-time data warehousing requires continuous delivery of data from the bookkeeping systems into the data warehouse repository. Old style batch data processing is not appropriate in the new world of real-time data warehousing. Batch file processing typically revolves around relatively infrequent (e.g., once per day or even less often) data acquisition. In the real-time enterprise the data spigot is always turned on. The implication is that ETL infrastructure must be stream oriented rather than file oriented. The leading ETL vendors have already begun to enhance their product offerings with adapters to allow stream processing as an additional option beyond file processing. These adapters typically interface with reliable queuing systems or publish/subscribe mechanisms within the EAI infrastructure for the enterprise.

Capacity planning for the data warehouse will need to be re-visited when the transition from batch to continuous data acquisition takes place. There will no longer be a shrinking batch window at night or on weekends to contend with after converting to real-time data acquisition. However, incremental workload throughout the day (and night) must be accounted for in the capacity plan to allow for simultaneous query execution and data acquisition within the real-time data warehouse. Moreover, the per-record resource requirements for data acquisition will be higher than in the batch processing world. The capacity plan must account for peak workload conditions when considering the resources necessary for implementation—do not fall into the trap of using average workload requirements.

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School of Computing - University of South Alabama

These re-accommodation decisions require integration of information from many different sources: flight schedules, boarding information, flight bookings, checked luggage, customer value, historical information regarding previously misconnected flights for each passenger, and much more. The real-time data warehouse is where this information all comes together. However, once the re-accommodation decisions are made, there is further cooperation with the bookkeeping systems that is required to put them into action. The real-time data warehouse needs to provide the decisions that have been made back to the bookkeeping systems so that seats can be held for the allocated passengers on the later connecting flights, luggage can be transferred to the appropriate flights, hotel rooms can be allocated to stranded passengers, etc. Sometimes a human intermediary is involved in the feedback loop and at other times process-level integration can take place on an automated basis using enterprise application integration (EAI) software. In either case, taking action requires cooperation between the real-time data warehouse (for decision making) and the bookkeeping systems (to put the decision into action).

Data Science, Big Data Analytics and Advanced practices …

Mistake 4: Too Much Summary Data
A common vehicle for enhancing performance in traditional data warehousing is to make use of summary tables or cubes. By pre-aggregating data along commonly used dimensions of analysis it is possible to avoid dynamic calculation of summary data during query execution. The theory is to invest in construction of summary tables when new data is acquired into the warehouse in order to avoid dynamically building the summaries over and over again during query execution. Many query tools and applications have built-in capability for “aggregate awareness” to allow automatic exploitation of summary data.

Advanced Data Analytics & Parallel Technologies Laboratory

However, reliance on pre-aggregated summary tables is usually undesirable in a real-time data warehouse environment. The higher data volatility in the real-time enterprise changes the performance economics of building summary tables. In the days when summary tables were built once per week (or even once per month) and then used all week (month) before they were re-built again, the many times that the summaries were accessed during query execution easily justified the investment in their construction. However, with real-time data warehousing the content in the warehouse changes much more frequently. This means fewer uses of the summary data in between summary builds. As the warehouse moves toward a true continuous update, the investment required to keep summary tables up to date becomes less and less attractive.

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