Deductive Databases

I would have the fields set up as first name (EMP_FIRST), middle initital, (EMP_INITIAL), last name (EMP_LAST), area code (EMP_AREACODE), phone (EMP_PHONE).

Data is ever increasing. We need a system to represent, store and manipulate complex information, detect correlations and patterns, construct data models etc. Furthermore, being independently maintained, data can change in time or even change its base structure, making it difficult for modelling systems to accommodate these changes. Current representation and storage systems are not very flexible in dealing with structural changes and also they are not powered with the ability of performing complex data manipulations of the sort mentioned above.

2, supported by the given code writing which The conceptual model, Pseudo code, and Flowcharting but I come up with the approach to solve the problem, it start’s great started and finishing system flowcharting is so amazing, because flowcharting system is very specific graphic and structural and this is to grate and wonderful processing system ever to learn.

DeWitt, Naughton,Shanmugasundaram, Tufte, Zhang (1999) Relational Databases for Querying XML Documents: Limitations and Opportunities. Retrieved from: http://mit.ittelkom.ac.id/courses/biological-engineering/20-453j-biomedical-information-technology-fall-2008/readings/inlining_vldb.pdf

Performing a sample query in Microsoft Access using QBE and using its tools to establish the query parameters gives a graphical representation of the query and on viewing the SQL within the program the user is presented with an accurately written piece of equivalent SQL code that performs an identical query.

Purpose: The purpose of this paper is to give you an opportunity to apply and demonstrate your understanding of the tools and principles covered in the class to a project of your choice.

Since 1960 and beyond the need for an efficient data management and retrieval of data has always been an issue due to the growing need in business and academia. To resolve these issues a number of databases models have been created. Relational databases allow data storage, retrieval and manipulation using a standard Structured Query Language (SQL). Until now, relational databases were an optimal enterprise storage choice. However, with an increase in growth of stored and analyzed data, relational databases have displayed a variety of limitations. The limitations of scalability, storage and efficiency of queries due to the large volumes of data [1] [2].

Information is collected into the database every time a person comes in for a test or an examination. From the demographic information that is updated at every visit to the MRI done last week, all this information is compiled into  databases and is used to fill in the longitudinal patient care history. Data are stored in these databases and can be retrieved when desired by using a database repository.There are many databases contained within a data repository. Some of the databases contained within the repository are disparate. The repository makes retrieval and integration possible. In order to make acheive the goal of data integration the data must go through a process of data transformation. To achieve data transformation the data must be

In order to obtain peer reviewed research articles related to the impact of the psychological contract on job satisfaction in community mental health care workers it is important to review research that has been completed in a variety of researchers and in different settings. The databases that were selected to locate peer reviewed articles for this paper where Academic Search Complete and ProQuest Psychology. Tutorials were available for each of these databases and ProQuest was used to learn how to navigate through the data base since it was not a familiar data base to this author.

Using ontologies to enrich the semantics of data for queries and interoperability is becoming increasingly important, but there is a lack of tools to ease the integration and use of ontologies in databases using standard query languages or query interfaces. ONTOBIO integrates the two schemes by Bio medical concepts using a set of semantic rules. We have applied our method to a knowledge base of autism phenotype definitions, which are modeled using the web ontology language, and its rule language, SWRL. The system provides an interface for executing queries and bio ontologies which organize taxonomies information. This paper addresses about attributes and describes relationships between biomedical concepts. The system is also compatible for integration and deployable with databases and description logic based ontologies. Semantically annotated biomedical databases thus can be easily extended with powerful and expressive semantic enabled queries with DBOntoLink to use major ontologies hosted at NCBO BioPortal, with high query efficiency achieved through caching

Introduction: A company called Ian’s & Co currently employs a team of IT technicians to manage their IT infrastructure and also support the IT users. Also quite recently the company has taken over a similar but a smaller company which is also employs technical support staff in the same way.

Personal privacy today is a controversial and complex topic, which is influenced by a number of factors. There is an integral role that databases play in this highly debated topic. The fact that many people now carry out their transactions electronically is another important factor. There is also pressure on personal privacy for increased national security around the world to combat terrorism. In addition, personal privacy is even threatened by commercial factors and the Internet.

Digging information from a database sometimes requires creating complicated queries. Often a simple select * from . . . is nested into another one and both into another one and so forth until the result becomes a very complicated query containing scalar queries (subqueries in the select clause) and in-line views (subqueries in the from clause). Such queries are difficult to read, hard to maintain, and a nightmare to optimize.

The Semantic Web can be seen as an ever growing web of structured and interlinked data. Examples for large repositories of such data available in RDF are DBpedia, TAP and DBLP. Resource Description Framework (RDF) is a W3C standard to capture resource information in real-world applications such as social networks, biological databases, and the data analysis in the semantic Web. RDF graphs constitute the backbone of the semantic Web, and feature billions of interconnected facts (or RDF triples) that are published on the Web. Thus, it is very useful and important to study efficient answering of various queries over such RDF graphs.

COUNT-DECISION-TREE is an algorithm for sampling a hidden database with TOP-k-COUNT interface. In order to develop an algorithm, an attribute-order tree to a decision tree on the hidden database is generalized, since the decision tree allows each level of it to contain different attribute. With the help of figures 1 a) and b) in (Arjun Dasgupta et.al, 2009, 4),