Shahidul Islam Khan

A New Technique for Database Fragmentation in Distributed Systems

Improving the performance of a database system is one of the key research issues now a day. Distributed processing is an effective way to improve reliability and performance of a database system. Distribution of data is a collection of fragmentation, allocation and replication processes. Previous research works provided fragmentation solution based on empirical data about the type and frequency of the queries submitted to a centralized system. These solutions are not suitable at the initial stage of a database design for a distributed system. In this paper we have presented a fragmentation technique that can be applied at the initial stage as well as in later stages of a distributed database system for partitioning the relations. Allocation of fragments is done simultaneously in our algorithm. Result shows that proposed technique can solve initial fragmentation problem of relational databases for distributed systems properly. General Terms Database, Distributed database, Fragmentation

Privacy and security problems of national health data warehouse: a convenient solution for developing countries

Healthcare providers and researchers can discover the hidden knowledge from different health repositories if integration of data is performed by data warehousing. Integration of health records requires linkage of patients data in different heterogeneous sources. Preserving record linkage in National Health Data Warehouse, by retaining identifiable attributes, is essential for effective data mining as well. In contrast identifiable health data have high risk to patient privacy and make the health information systems security vulnerable to hackers. In this paper, we have provided a practical solution: Global Patient Identification Technique (GPIT) that can anonymize identifiable private data of the patients while maintaining record linkage in integrated health repositories to facilitate knowledge discovery process. We have used encrypted mobile number, gender and NAMEVALUE of patients to produce Global Patient Identification Key. This system is being implemented in Bangladesh to develop National Health Data Warehouse. Our approach is also suitable for the developing countries where poverty and illiteracy rates are high among mass people.

Towards Development of National Health Data Warehouse for Knowledge Discovery

Availability of accurate data on time is essential for medical decision making. Healthcare organizations own a large amount of data in various systems. Researchers, health care providers and patients will not be able to utilize the knowledge in different stores unless integration of the information from disparate sources is completed. Developing health data warehouse is a complex process and also consumes a significant amount of time but it is essential to deliver quality health services. In this paper the architecture of a data warehouse model and the development process suitable for integrating data from different healthcare sources have been presented. We have developed a Star schema suitable for large data warehouse. Integrating health data requires a rigorous preprocessing and we have completed the preprocessing of national health data by applying efficient transformation techniques. Finally the knowledge discovery potentials from the data warehouse are also presented with relevant examples.

Towards development of health Data Warehouse: Bangladesh perspective

Health informatics is one of the top most focuses of researchers now a days. Availability of timely and accurate data is essential for informed medical decision making. Health care organizations face a common problem with large amount of data they have in numerous systems. Such systems are unstructured and unorganized, requires computational time for data integration. Researchers, medical practitioners, health care providers and patients will not be able to utilize the knowledge stored in different repositories unless synthesize the information from disparate sources. This problem can be solved by Data warehousing. Data warehousing techniques share a common set of tasks, include requirements analysis, data design, architectural design, implementation and deployment. Developing Clinical data warehouse is complex and time consuming but is essential to deliver quality patient care. Data integration tasks of medical data store are much challenging when designing clinical data warehouse architecture. This research identifies prospects and complexities of Health data warehousing and Mining in Bangladesh perspective and proposes a data-warehousing model suitable for integrating data from different health care sources.

Health data integration with Secured Record Linkage: A practical solution for Bangladesh and other developing countries

Knowledge discovery from various health data repositories requires the incorporation of healthcare data from diversified sources. Maintaining record linkage during the integration of medical data is an important research issue. Researchers have given different solutions to this problem that are applicable for developed countries where electronic health record of patients are maintained with identifiers like social security number (SSN), universal patient identifier (UPI), health insurance number, etc. These solutions cannot be used correctly for record linkage of health data of developing countries because of missing data, ambiguity in patient identification, and high amount of noise in patient information. Also, identifiable health data in electronic health repositories may produce a significant risk to patient privacy and also make the health information systems security vulnerable to hackers. In this paper, we have analyzed the practical problems of collecting and integrating healthcare data in Bangladesh for developing national health data warehouse. We have proposed a privacy preserved secured record linkage architecture that can support constrained health data of developing countries such as Bangladesh. Our technique can anonymize identifiable private data of the patients while maintaining record linkage in integrated health repositories to facilitate knowledge discovery process. Experimental results show that our proposed method successfully linked records with acceptable accuracy for noisy data in the absence of any standard ID like SSN.

An analysis of the problems for Health Data integration in Bangladesh

Discovering the hidden knowledge from different health data repositories requires the integration of health data from widely diversified sources. While integration, maintaining record linkage is a critical research issue for developing countries such as Bangladesh. Researchers have given different solutions to this problem that are applicable for developed countries where electronic health record of patients are maintained with identifiers like social security number (SSN), Universal Patient Identifier (UPI), etc. These algorithms cannot be used for integrating health data in Bangladesh because of many missing data, different ID used for the same patient in same or different systems and high amount of noise in other patient information. In this paper, we have defined the constraining factors of healthcare date available in Bangladesh and other developing countries. We have analyzed the practical problems of collecting and integrating healthcare data in Bangladesh to build up its National Health Data Warehouse (NHDW). We have also provided some important recommendations to boost the integration process and also to support record linkage.

Similarity analysis of patients' data: Bangladesh perspective

Misspelling of names is a major problem of real world datasets and a single person is identified differently as its consequence. In Bangladesh, it is common that many people, in real, do not know their full name and many of Bangladeshi citizens are unable to pronounce their name correctly, even in the mother tongue. The Same person provides a different version of their name during taking a public service e.g., treatment in hospital. In almost all healthcare centers, a patient is asked and he reports his demographic data i.e. name, age, etc. orally. This creates ambiguity with misspelled names. In this paper, we have provided an algorithm to identify the same person correctly from the variation of names. Experimental results show that our proposed technique can successfully link records with high accuracy for noisy data like misspelled patient names etc.