A Data Warehouse Based Schema Design on Decision-Making in Loan Disbursement for Indian Advance Sector
01 Jan 2019-pp 603-614
TL;DR: In this paper, the authors proposed a data warehouse model which integrates the existing parameters of loan disbursement related decisions and also incorporates the newly identified concepts to give the priorities to the customers who don't have any old credit history.
Abstract: Disbursement of loan is an important decision-making process for the corporate like banks and NBFC (Non-banking Finance Corporation) those offers loans. The business involves several parameters and the data which are associated to these parameters are generated from heterogeneous data sources and also belong to different business verticals. Henceforth the decision-making on loan scenarios are critical and the outcome involve solving the issues like whether to grant the loan or not, if sanctioned what is highest amount, etc. In this paper we consider the traditional parameters of loan sanction process along with these we identify one special case of Indian credit lending scenario where the people having old loans with good repayment history get priority. This limits the business opportunities for Bank/NBFC or other loan disbursement organizations as potential good customers having no loan history are treated with less priority. In this research work we propose a data warehouse model which integrates the existing parameters of loan disbursement related decisions and also incorporates the newly identified concepts to give the priorities to the customers who don’t have any old credit history.
Citations
More filters
TL;DR: This paper describes a framework which is in the form of an extension to Unified Modelling Language (UML) which focuses on the accurate representations of the properties of the MD systems based on domain specific information.
Abstract: Data Warehouse (DW) applications provide past detail for judgment process for the companies. It is acknowledged that these systems depend on Multidimensional (MD) modelling different from traditional database modelling. MD modelling keeps data in the form of facts and dimensions. Some proposals have been presented to achieve the modelling of these systems, but none of them covers the MD modelling completely. There is no any approach which considers all the major components of MD systems. Some proposals provide their proprietary visual notations, which force the architects to gain knowledge of new precise model. This paper describes a framework which is in the form of an extension to Unified Modelling Language (UML). UML is worldwide known to design a variety of perspectives of software systems. Therefore, any method using the UML reduces the endeavour of designers in understanding the novel notations. Another exceptional characteristic of the UML is that it can be extended to bring in novel elements for different domains. In addition, the proposed UML profile focuses on the accurate representations of the properties of the MD systems based on domain specific information. The proposed framework is validated using a specific case study. Moreover, an evaluation and comparative analysis of the proposed framework is also provided to show the efficiency of the proposed work.
3 citations
01 Jan 2021
TL;DR: In this paper, a data warehouse for bank data relating to consumers, goods, services, etc. has been presented, where the implementation steps of Kimball lifecycle have been presented followed by the ETL process for bank customer's data.
Abstract: In today’s world, the banking sector has played a key role in the financial development of a country. Generally, in banking sector, there are many types of historical data in multiple heterogeneous databases, and posing queries on these heterogeneous databases is a very complex process. Since banks are running digitally—and generating numerous data—it is a simple transformation to attain a better way to use that data. Therefore, the increasing competition of market changes has demanded bank intelligence for analyzing those enormous data. In this paper, we construct a data warehouse and present the data warehouse applicability in the investigation of the banking data relating to consumers, goods, services, etc. At first, the implementation steps of Kimball lifecycle have been presented followed by the ETL process for bank customer’s data. Afterward, OLAP cube has been developed using Microsoft Visual Studio 2019. Finally, OLAP analysis has been done using Microsoft power BI. The experimental result has unveiled the uniformity and strength of OLAP-based solutions to expansible bank intelligence.
3 citations
Posted Content•
TL;DR: Techniques to exploit the advantages of multicore architecture is studied to address solving graph problems of Big Data and Internet of Things.
Abstract: With the advent of era of Big Data and Internet of Things, there has been an exponential increase in the availability of large data sets. These data sets require in-depth analysis that provides intelligence for improvements in methods for academia and industry. Majority of the data sets are represented and available in the form of graphs. Therefore, the problem at hand is to address solving graph problems. Since the data sets are large, the time it takes to analyze the data is significant. Hence, in this paper, we explore techniques that can exploit existing multicore architecture to address the issue. Currently, most Central Processing Units have incorporated multicore design; in addition, co-processors such as Graphics Processing Units have large number of cores that can used to gain significant speedup. Therefore, in this paper techniques to exploit the advantages of multicore architecture is studied.
2 citations
Cites background from "A Data Warehouse Based Schema Desig..."
...But the other concern with graph algorithms is their random data access, whereby simple techniques like tiling and blocking which work in the case of other algorithms, is not always a good choice to address the matter here [19] [23]....
[...]
Posted Content•
TL;DR: This paper proposes techniques to compress the adjacency matrix representation of the graph and shows that large graphs can be efficiently stored in smaller memory and exploit the parallel processing power of compute nodes as well as efficiently transfer data between resources.
Abstract: Graphs can be used to represent a wide variety of data belonging to different domains. Graphs can capture the relationship among data in an efficient way, and have been widely used. In recent times, with the advent of Big Data, there has been a need to store and compute on large data sets efficiently. However, considering the size of the data sets in question, finding optimal methods to store and process the data has been a challenge. Therefore, in this paper, we study different graph compression techniques and propose novel algorithms to do the same. Specifically, given a graph G = (V, E), where V is the set of vertices and E is the set of edges, and |V| = n, we propose techniques to compress the adjacency matrix representation of the graph. Our algorithms are based on finding patterns within the adjacency matrix data, and replacing the common patterns with specific markers. All the techniques proposed here are lossless compression of graphs. Based on the experimental results, it is observed that our proposed techniques achieve almost 70% compression as compared to adjacency matrix representation. The results show that large graphs can be efficiently stored in smaller memory and exploit the parallel processing power of compute nodes as well as efficiently transfer data between resources.
1 citations
Cites background from "A Data Warehouse Based Schema Desig..."
...Each of the data structures has its advantages and disadvantages with respect to the amount of memory required to store the data and the ease of access to the data [21] [22]....
[...]
Posted Content•
TL;DR: This paper proposes techniques to compress graphs by finding specific patterns and replacing those with identifiers that are of variable length, an idea inspired by Huffman Coding, to reduce the space requirements of the graph by compressing the adjacency representation of the same.
Abstract: Graphs have been extensively used to represent data from various domains. In the era of Big Data, information is being generated at a fast pace, and analyzing the same is a challenge. Various methods have been proposed to speed up the analysis of the data and also mining it for information. All of this often involves using a massive array of compute nodes, and transmitting the data over the network. Of course, with the huge quantity of data, this poses a major issue to the task of gathering intelligence from data. Therefore, in order to address such issues with Big Data, using data compression techniques is a viable option. Since graphs represent most real world data, methods to compress graphs have been in the forefront of such endeavors. In this paper we propose techniques to compress graphs by finding specific patterns and replacing those with identifiers that are of variable length, an idea inspired by Huffman Coding. Specifically, given a graph G = (V, E), where V is the set of vertices and E is the set of edges, and |V| = n, we propose methods to reduce the space requirements of the graph by compressing the adjacency representation of the same. The proposed methods show up to 80% reduction is the space required to store the graphs as compared to using the adjacency matrix. The methods can also be applied to other representations as well. The proposed techniques help solve the issues related to computing on the graphs on resources limited compute nodes, as well as reduce the latency for transfer of data over the network in case of distributed computing.
Cites result from "A Data Warehouse Based Schema Desig..."
...Therefore, our results and observations are significant for real world data [20] [21] [22]....
[...]
References
More filters
DOI•
31 Dec 2009
TL;DR: In this paper, the authors studied the priority sector advances by the public, private and foreign bank groups and concluded that public sector banks have not achieved the target of 40% while private sector banks had achieved the overall target.
Abstract: An enunciation of the need to channelise the flow of credit to certain sectors of the economy, known as the priority sectors, in the largest interest of the country, can be traced to the Reserve Bank’s credit policy for the year 1967 - 1968. The government initiated measures for social control over banks in 1967 - 1968 with a view to securing a better adaptation of the banking system to the needs of economic planning and it is playing a more active role in aiding sectors like agriculture and small scale industries (SSIs). The present study is an attempt to study the priority sector advances by the public, private and foreign bank groups. This study is based on the parameters like lending to priority sector by public, private sector and foreign bank groups, targets achieved by public, private sector and foreign bank group NPAs (Non-performing assets), while lending to priority sector. On the basis of these parameters, the study concludes that public sector banks have not achieved the target of 40% while private sector banks have achieved the overall target. No private sector bank could achieve the 10% target by lending to weaker section. On the other hand, foreign banks have achieved the small scale industries’ export credit and overall target. NPAs of public sector banks have increased because of high priority sector advances. The paper also throw light on the problems or issues which arise due to priority sector advances and also suggest some strategies to sought out these issues. All the parameters have been analyzed for the period, 2006 - 2007.
Key words: Priority sector advances, targets achieved, issues, strategies.
43 citations
24 Nov 2009
TL;DR: An optimal aggregation and counter-aggregation (drill-down) methodology is proposed on multidimensional data cube to aggregate on smaller cuboids after partitioning those depending on the cardinality of the individual dimensions.
Abstract: In this paper, an optimal aggregation and counter-aggregation (drill-down) methodology is proposed on multidimensional data cube. The main idea is to aggregate on smaller cuboids after partitioning those depending on the cardinality of the individual dimensions. Based on the operations to make these partitions, a Galois Connection is identified for formal analysis that allow to guarantee the soundness of optimizations of storage space and time complexity for the abstraction and concretization functions defined on the lattice structure. Our contribution can be seen as an application to OLAP operations on multidimensional data model in the Abstract Interpretation framework.
18 citations
TL;DR: This research work dynamically finds the most cost effective path from the lattice structure of cuboids based on concept hierarchy to minimize the query access time.
13 citations
01 Dec 2015
TL;DR: A suitable Data warehouse schema is proposed which comprises of the required dimensions along with their concept hierarchies and the lattice of cuboids is constructed to carry out the OLAP processing from all possible business perspective.
Abstract: Each mobile device represents the digital footprint of the owner; at the same time mobile location data stored in telecom operators' databases in terms of Call Detail Record (CDR). It holds the precise identity of the mobile cell tower to which the owner is connected at any given time. Effectively mobile device count within a region for some time period can be calculated. Again, International Mobile Equipment Identity (IMEI) number is an unique identity to every mobile device, a part of which, known as Type Allocation Code (TAC) uniquely identifies the make and model of the mobile device which further identify the company or manufacturer of the mobile device. So combining them it is possible to analyze different business information about mobile penetration of companies in a defined region; hence the localized market share comparisons with other companies as well as among different models of same company. In order to model the problem and analyze huge CDR data, an analytical processing is carried out here using data warehouse. Here we propose a suitable Data warehouse schema which comprises of the required dimensions along with their concept hierarchies. The ETL processing which is done to form the data warehouse is described here. Finally the lattice of cuboids is constructed to carry out the OLAP processing from all possible business perspective.
12 citations
TL;DR: A schema selection framework that is based on a decision tree for solving the problem of choosing right schema for a data warehouse and the main selection criteria that are used in the presented decision tree are query type, attribute type, dimension table type and existence of index.
Abstract: Data schema represents the arrangement of fact table and dimension tables and the relations between them. In data warehouse development, selecting a right and appropriate data schema (Snowflake, Star, Star Cluster …) has an important Impact on performance and usability of the designed data warehouse. One of the problems that exists in data warehouse development is lack of a comprehensive and sound selection framework to choose an appropriate schema for the data warehouse at hand by considering application domain-specific conditions. In this paper, we present a schema selection framework that is based on a decision tree for solving the problem of choosing right schema for a data warehouse. The main selection criteria that are used in the presented decision tree are query type, attribute type, dimension table type and existence of index. To evaluate correctness and soundness of this framework, we have designed a test bed that includes multiple data warehouses and we have created all the possible states in decision tree of schema selection framework. Then we designed all types of queries and performed the designed queries on these data warehouses. The results confirm the correct functionality of the schema selection framework.
6 citations