Dinesh Asanka

Oil is one of the most crucial commodity and energy resource that guarantee the evolution of the economy and industry of a country. The price fluctuation of the oil would be the emerging factor to be concerned and discussed generally in political and economic circle in each and every country. In Sri Lanka also the problem would be same when it comes to oil industry and its influencing factors for price fluctuation. This paper provides a comprehensive implementation of data warehousing process for the petroleum industry data set. Based on the data collection, a broad data analysis has been conducted to discover patterns of oil prices and sales variation with respect to the political and economic impact factors of Sri Lanka. Through the analysis, it has proven that the influencing factors would affect to the oil prices and sales accordingly. Furthermore, this aims to present few predictions based on the analysis.

The Consumer Financial Protection Bureau was established in USA for enabling the USA consumers to report customer support and complaint related information regarding their financial issues with the US government. The complaint data is freely available for analysis and tracking of how efficiently and effectively the financial institutes handle the complaints lodged against them. Each complaint consists of attributes that can uniquely describe and identify it. These features have been exploited for data mining, analysis and predictions. The data warehouse creation and data analysis was done using Microsoft SQL Server Technologies. The data mining techniques such as Microsoft Decision Tree, Microsoft Naïve Bayes, Microsoft Time Series and Microsoft Neural Network models were used in this study. Based on the results, it was observed that there is a correlation between the growth of complaints in certain financial domains with regards to changes in the economic, political and regulatory forces. Probability predictions also show, how each product can get a particular issue-related complaint, how a particular issue can get a timely response, how a particular issue can cause a consumer dispute and what type of issues are mostly lodged via a particular submission method, etc. This information can be used in prescriptive analysis to enhance financial consumer services and also improve the response quality of automated consumer support systems.

Data warehouse is used in many organization as a tool to gain competitive advantage over their competitors. However, in most data warehouse implementations, lot of assumptions are made. By making assumptions, outcome of data warehouse is little far from the truth. Also, these assumptions will veracity of data warehouse is questionable. To avoid veracity of data warehouse, fuzzy logic can be incorporated. In fuzzy logic, fuzzy membership function plays a huge role hence in case of fuzzy data warehouse, fuzzy membership plays a key role as well. In data warehouse, different types of fuzzy membership functions can be introduced. arbitrary, data driven, linguistic, derived, survey-based membership functions are introduced in this research paper for different cases in data warehouse.

Oil is one of the most crucial commodity and energy resource that guarantee the evolution of the economy and industry of a country. The price fluctuation of the oil would be the emerging factor to be concerned and discussed generally in political and economic circle in each and every country. In Sri Lanka also the problem would be same when it comes to oil industry and its influencing factors for price fluctuation. This paper provides a comprehensive implementation of data warehousing process for the petroleum industry data set. Based on the data collection, a broad data analysis has been conducted to discover patterns of oil prices and sales variation with respect to the political and economic impact factors of Sri Lanka. Through the analysis, it has proven that the influencing factors would affect to the oil prices and sales accordingly. Furthermore, this aims to present few predictions based on the analysis.

This research describes a framework to find out most relevant information related web sources according to users’ preferences. Since throughout the literature survey has identified that existing search engines did not return the accurate results and to over come this issue, the research has conducted to pull out most accurate results to be returned according to users search criteria using text mining technique. This relevant framework defined to identify the relevant web sources to crawl the specified information using web crawlers, in order to build the necessary dataset. The analysis has taken over in two ways as Web contents and Web content’s titles. The created data set passed to generate keywords, these keywords have taken to apply “Association Rule” data mining technique to train and this trained data set have verified using test data to ensure the accuracy of developed framework. As per the research, result shows most relevant Web URLs related to users’ search. These results are directed to analyze which way of web source will be better to use for Web content mining framework such as Web contents or content’s titles.

A variety of software projects were considered based on factors such as the business nature of the organization, place of implementation, type of software, number of users of software and investment for the software. To identify the reasons for software implementation ...

In any organization, the leadership is responsible for taking decisions that will lift the said organization to a better place. The problem-solving abilities of the management are mostly depending on the ability to grasp all the required information in a clean and actionable format. Building a well-designed data warehouse leads to answer that problem. When data sourced from different sourcing systems, it's very important that the aggregated data is relevant and supporting the decision-making by the leadership. This study aims at mitigating the issues that are hindering such organizations to make correct decisions.

Data warehouse is used in many organization as a tool to gain competitive advantage over their competitors. However, in most data warehouse implementations, lot of assumptions are made. By making assumptions, outcome of data warehouse is little far from the truth. Also, these assumptions will veracity of data warehouse is questionable. To avoid veracity of data warehouse, fuzzy logic can be incorporated. In fuzzy logic, fuzzy membership function plays a huge role hence in case of fuzzy data warehouse, fuzzy membership plays a key role as well. In data warehouse, different types of fuzzy membership functions can be introduced. arbitrary, data driven, linguistic, derived, survey-based membership functions are introduced in this research paper for different cases in data warehouse.

Effective and efficient data management is key to today’s competitive business environment. Data is a valuable asset for any organization. Data is information and information is knowledge. Today’s enterprise applications generate large amount of data especially because of high usage of the internet. As a result, enterprise applications should be able to scale out and perform as required. Otherwise, these applications will not be able to handle their data load, leading to trouble in business continuity. Data replication is one widely used phenomenon in distributed environments, where data is stored in multiple sites, within same or differing geographical areas. Thus enterprise applications will be able to scale out, so that they perform well. Many modern Database Management Systems (DBMSs) provide in-built methods for data replication. Replication is possible between heterogeneous database systems. In this research, the aim is to design and implement a middleware layer for data repli...

There is a gap when representing detailed information related to public on maps due to limitations of underlying data sources. The purpose of this research is to scan the research output on Geographical Information Systems (GIS) and relational databases, then develop an implementation by combining these two technologies GIS and graph databases in order to fill the identified research gap. The outcome of this implementation is a central Geospatial intelligence on graph which delivers benefits in the context of multiple data source integration and open sources.

This paper has examined propagations and deliberations of AI in Sri Lankan software industry. A survey has been carried out for gathering the information. Even though software industry is a rapidly growing sector in Sri Lanka, it is lagging behind in terms of using AI technologies with compared to other countries in the world. According to the survey, this is due to the lack of popularity, knowledge, experts, requirements and sponsorship for the AI related software projects. Sri Lankan software industry has the required maturity to adopt with AI technologies and they have already made their foot print on potential markets where they could find advanced AI research and development projects in different domains. However, Sri Lankan software industry has not been able to achieve the required momentum, stability and the confidence on research and development projects in AI. Large-scale companies must increase their attention on AI technologies and the required knowledge and work force s...

Effective and efficient data management is key to today's competitive business environment. Data is a valuable asset for any organization. Data is information and information is knowledge. Today's enterprise applications generate large amount of data especially because of high usage of the internet. As a result, enterprise applications should be able to scale out and perform as required. Otherwise, these applications will not be able to handle their data load, leading to trouble in business continuity. Data replication is one widely used phenomenon in distributed environments, where data is stored in multiple sites, within same or differing geographical areas. Thus enterprise applications will be able to scale out, so that they perform well. Many modern Database Management Systems (DBMSs) provide in-built methods for data replication. Replication is possible between heterogeneous database systems. In this research, the aim is to design and implement a middleware layer for data replication from RDBMS to document oriented DBMS. The middleware layer includes a Java program, source and destination DBMSs. The research approach is to capture DML/DDL changes in source relational DBMS and then convert and stores them in an intermediate XML format. Then the java program continuously looks for such data changes and then push them to the destination DBMS. A replication method like this can address the need of application scalability in situations where both types of DBMSs are used. In live environments, there are areas of possible performance improvements to the middleware layer, especially when dealing with large data volumes.

There is a common belief in cricket that extra delivery in an over will costly more than to the bowling team than to the batting team. This research paper is to verify the above statement. Many parameters were identified and this research is to verify what the leading factors which will affect the outcome of the outcome extra delivery. Apart from the standard statically analysis data warehousing and data mining approach will be used. After analysis data, it was discovered that more than individual factors, team cricket status will more matter to the outcome of the extra delivery.

Oil is one of the most crucial commodity and energy resource that guarantee the evolution of the economy and industry of a country. The price fluctuation of the oil would be the emerging factor to be concerned and discussed generally in political and economic circle in each and every country. In Sri Lanka also the problem would be same when it comes to oil industry and its influencing factors for price fluctuation. This paper provides a comprehensive implementation of data warehousing process for the petroleum industry data set. Based on the data collection, a broad data analysis has been conducted to discover patterns of oil prices and sales variation with respect to the political and economic impact factors of Sri Lanka. Through the analysis, it has proven that the influencing factors would affect to the oil prices and sales accordingly. Furthermore, this aims to present few predictions based on the analysis.

Many organizations are using Data warehouse as a strategical decision making tool. Over the years, major emphasis was placed to analytics purposes. Uncertainty is one of the major challenge faced in many areas such as data warehouse, database etc. In case of uncertainty, fuzzy technique can be utilized. This research paper is to carry out a feasibility study whether is the fuzzy data warehouse implementation would be a viable solution. Also, this research paper explorer the domains where fuzzy data warehousing can be implemented.

Data warehouse is used intensively in many industry domains to gain competitive advantage over it's competitors. In modern data warehouse, linguistics analytics is important so that it has the ability to take more precious decisions. In most of the data warehouse implementations, it is designed for crisp analysis. Crisp analysis has its own limitations and boundaries with the major assumptions that every situation is belongs to one state and denial to other states. Hence crisp data warehouse does not allow to carry out linguistic analytics. When fuzzy data warehouse is implemented because of the fuzzy nature of the data warehouse, linguistics analytics can be done. In this research, non-functional requirement such as performance and configuration are also covered so that this method can be implemented.

Data warehouse is used intensively in the industry to gain competitive advantage over it's competitors. In traditional data warehouse, more crisp values are being used so that substantial information is lost. This will lead to limit the true analysis of data warehouse resulting incorrect decision making. Since data warehouse is used for analysis and decision making, by introducing crisp values will reduce the accuracy of outcome. To overcome this fuzzy technique is proposed. In this research paper, fuzzy techniques are introduced to design data warehouse incorporate better analysis keeping the historical aspect of data in data warehouses.