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In recent years, many people use the World Wide Web and Internet to find information that they want. HTML is a document markup language for publishing hypertext on the WWW. HTML has been the target format for content developers around the world. Basically, HTML tags serve the primary purpose of describing how to display a data item. Therefore, HTML documents are difficult to find some useful information. That is because, HTML documents are mixed content with display tags. On the other hand, XML is the another data format for data exchange inter-enterprise applications on the Internet. In order to facilitate data exchange, industry groups define public Document Type Definitions (DTD) that specify the format of the XML documents to be exchanged between their applications. Moreover, WWW/EDI or Electric Commerce is very popular and a lot of business data uses XML to exchange on the World Wide Web. Basically, XML tags describe the data itself. The contents (meaning) of the XML documents and the display format is separated. It could be easily to find meaningful information of the XML documents and analyze the information. Moreover, when a large volume of business data (XML documents) exists, we must transform the XML documents to the relational databases. In order to exchange business data between applications, we must construct the XML documents from the relational database. In this thesis, we design the mapping technique and present the implementation of mapping tools between XML documents and relational databases. XML document is fundamentally different from relational data. XML document are hierarchy, and elements of document should be nested and repeated more times (i.e., set-valued and recursion). Therefore, we can not map from the XML documents to the relational databases straightforwardly. Our mapping technique must resolve the above problems. We design and implement a mapping technique between the XML documents and the relational database such that those mapping can be done automatically for any kind of XML documents and any kind of commercial relational databases. The whole tools are implemented in Visual Basic and SQL Server 2000. From our experiences, we show that our efficient mapping technique can be applied to any kind of relational databases without any extra requirements or changes to the databases.

With the increasing usage of storing textual data into relational databases, there is a demand for the databases to support keyword queries over textual data. Due to the normalization and the inherent connections among tuples in different tables, traditional IR-style ranking and query evaluation methods do not apply. A number of systems have been proposed to deal with this issue. In this thesis, I will give a detailed demonstration and description to our SPARK project. In the project, we study both the effectiveness and the efficiency issues of answering top-k keyword query on a relational database system. We propose a new ranking formula by adapting existing IR techniques on a natural notion of ???virtual document???. Compared with previous approaches, our new ranking method is simple yet effective, and agrees with human being???s perception better. We also study efficient query processing methods based on the new ranking method, and propose algorithms that have minimal accesses to the database. We have conducted extensive experiments on large-scale real databases using two popular RDBMSs. The experimental results demonstrate significant improvement to the alternative approaches in terms of both retrieval effectiveness and efficiency. We build a prototype of SPARK system on top of popular RDBMS based on these new techniques to satisfy different kinds of users and to support various query modes.

International Telemetering Conference Proceedings / October 20-23, 2003 / Riviera Hotel and Convention Center, Las Vegas, Nevada / One problem facing telemetry engineers is the ability to easily translate telemetry attributes from one system to another. Engineers must develop a written set of attributes that define a given telemetry stream and specify how the telemetry stream is to be transmitted, received, and processed. Telemetry engineers take this document and create the configuration for each product that will be exposed to the telemetry stream (airborne, ground, flight line). This process is time-consuming and prone to error. L-3 Telemetry-West chose to implement a solution using relational databases and eXtensible Markup Language (XML) to solve this and other issues.

Published Article / To enable meaningful and consistent communication between different software systems in a particular domain (such as manufacturing, law or medicine), a standardised vocabulary and communication language is required by all the systems involved. Concepts in the domain about which the systems want to communicate are formalized in an ontology by establishing the meaning of concepts and creating relationships between them. The inputs to this process in found by analysing the physical domain and its processes. The resulting ontology structure is a computer useable representation of the physical domain about which the systems want to communicate. To enable the long term persistence of the actual data contained in these concepts and the enforcement of various business rules, a sufficiently powerful database system is required. This paper presents the design of a manufacturing ontology and its accompanying relational database schema that will be used in a manufacturing test domain.

This research provides improved support for missing data in the relational model and relational database systems. There is a need for a systematic method to represent and interpret missing data values in the relational model. A system that processes missing data needs to enable making reasonable decisions when some data values are unknown. The user must be able to understand query results with respect to these decisions. While a number of approaches have been suggested, none have been completely implemented in a relational database system. This research describes a missing data model that works within the relational model, is implemented in MySQL, and was validated by a user feasibility study.

The term quote semantic query optimization quote (SQO) denotes a methodology whereby queries against databases are optimized using semantic information about the database objects being queried. The result of semantically optimizing a query is another query which is syntactically different to the original, but semantically equivalent and which may be answered more efficiently than the original. SQO is distinctly different from the work performed by the conventional SQL optimizer. The SQL optimizer generates a set of logically equivalent alternative execution paths based ultimately on the rules of relational algebra. However, only a small proportion of the readily available semantic information is utilised by current SQL optimizers. Researchers in SQO agree that SQO can be very effective. However, after some twenty years of research into SQO, there is still no commercial implementation. In this thesis we argue that we need to quantify the conditions for which SQO is worthwhile. We investigate what these conditions are and apply this knowledge to relational database management systems (RDBMS) with static schemas and infrequently updated data. Any semantic query optimizer requires the ability to reason using the semantic information available, in order to draw conclusions which ultimately facilitate the recasting of the original query into a form which can be answered more efficiently. This reasoning engine is currently not part of any commercial RDBMS implementation. We show how a practical semantic query optimizer may be built utilising readily available semantic information, much of it already captured by meta-data typically stored in commercial RDBMS. We develop cost models which predict an upper bound to the amount of optimization one can expect when queries are pre-processed by a semantic optimizer. We present a series of empirical results to confirm the effectiveness or otherwise of various types of SQO and demonstrate the circumstances under which SQO can be effective.

Most existing data mining approaches perform data mining tasks on a single data table. However, increasingly, data repositories such as financial data and medical records, amongst others, are stored in relational databases. The inability of applying traditional data mining techniques directly on such relational database thus poses a serious challenge. To address this issue, a number of researchers convert a relational database into one or more flat files and then apply traditional data mining algorithms. The above-mentioned process of transforming a relational database into one or more flat files usually involves aggregation. Aggregation functions such as maximum, minimum, average, standard deviation, count and sum are commonly used in such a flattening process. Our research aims to address the following question: Is there a link between aggregation and possible privacy violations during relational database mining? In this research we investigate how, and if, applying aggregation functions will affect the privacy of a relational database, during supervised learning, or classification, where the target concept is known. To this end, we introduce the PBIRD (Privacy Breach Investigation in Relational Databases) methodology. The PBIRD methodology combines multi-view learning with feature selection, to discover the potentially dangerous sets of features as hidden within a database. Our approach creates a number of views, which consist of subsets of the data, with and without aggregation. Then, by identifying and investigating the set of selected features in each view, potential privacy breaches are detected. In this way, our PBIRD algorithm is able to discover those features that are correlated with the classification target that may also lead to revealing of sensitive information in the database. Our experimental results show that aggregation functions do, indeed, change the correlation between attributes and the classification target. We show that with aggregation, we obtain a set of features which can be accurately linked to the classification target and used to predict (with high accuracy) the confidential information. On the other hand, the results show that, without aggregation we obtain another different set of potentially harmful features. By identifying the complete set of potentially dangerous attributes, the PBIRD methodology provides a solution where the database designers/owners can be warned, to subsequently perform necessary adjustments to protect the privacy of the relational database. In our research, we also perform a comparative study to investigate the impact of aggregation on the classification accuracy and on the time required to build the models. Our results suggest that in the case where a database consists only of categorical data, aggregation should especially be used with caution. This is due to the fact that aggregation causes a decrease in overall accuracies of the resulting models. When the database contains mixed attributes, the results show that the accuracies without aggregation and with aggregation are comparable. However, even in such scenarios, schemas without aggregation tend to slightly outperform. With regard to the impact of aggregation on the model building time, the results show that, in general, the models constructed with aggregation require shorter building time. However, when the database is small and consists of nominal attributes with high cardinality, aggregation causes a slower model building time.

Most existing data mining approaches perform data mining tasks on a single data table. However, increasingly, data repositories such as financial data and medical records, amongst others, are stored in relational databases. The inability of applying traditional data mining techniques directly on such relational database thus poses a serious challenge. To address this issue, a number of researchers convert a relational database into one or more flat files and then apply traditional data mining algorithms. The above-mentioned process of transforming a relational database into one or more flat files usually involves aggregation. Aggregation functions such as maximum, minimum, average, standard deviation, count and sum are commonly used in such a flattening process. Our research aims to address the following question: Is there a link between aggregation and possible privacy violations during relational database mining? In this research we investigate how, and if, applying aggregation functions will affect the privacy of a relational database, during supervised learning, or classification, where the target concept is known. To this end, we introduce the PBIRD (Privacy Breach Investigation in Relational Databases) methodology. The PBIRD methodology combines multi-view learning with feature selection, to discover the potentially dangerous sets of features as hidden within a database. Our approach creates a number of views, which consist of subsets of the data, with and without aggregation. Then, by identifying and investigating the set of selected features in each view, potential privacy breaches are detected. In this way, our PBIRD algorithm is able to discover those features that are correlated with the classification target that may also lead to revealing of sensitive information in the database. Our experimental results show that aggregation functions do, indeed, change the correlation between attributes and the classification target. We show that with aggregation, we obtain a set of features which can be accurately linked to the classification target and used to predict (with high accuracy) the confidential information. On the other hand, the results show that, without aggregation we obtain another different set of potentially harmful features. By identifying the complete set of potentially dangerous attributes, the PBIRD methodology provides a solution where the database designers/owners can be warned, to subsequently perform necessary adjustments to protect the privacy of the relational database. In our research, we also perform a comparative study to investigate the impact of aggregation on the classification accuracy and on the time required to build the models. Our results suggest that in the case where a database consists only of categorical data, aggregation should especially be used with caution. This is due to the fact that aggregation causes a decrease in overall accuracies of the resulting models. When the database contains mixed attributes, the results show that the accuracies without aggregation and with aggregation are comparable. However, even in such scenarios, schemas without aggregation tend to slightly outperform. With regard to the impact of aggregation on the model building time, the results show that, in general, the models constructed with aggregation require shorter building time. However, when the database is small and consists of nominal attributes with high cardinality, aggregation causes a slower model building time.

In recent years, many people use the World Wide Web and Internet to find information that they want. HTML is a document markup language for publishing hypertext on the WWW. HTML has been the target format for content developers around the world. Basically, HTML tags serve the primary purpose of describing how to display a data item. Therefore, HTML documents are difficult to find some useful information. That is because, HTML documents are mixed content with display tags. On the other hand, XML is the another data format for data exchange inter-enterprise applications on the Internet. In order to facilitate data exchange, industry groups define public Document Type Definitions (DTD) that specify the format of the XML documents to be exchanged between their applications. Moreover, WWW/EDI or Electric Commerce is very popular and a lot of business data uses XML to exchange information on the World Wide Web. Basically, XML tags describe the data itself. The contents (meaning) of the XML documents and the display format is separated. It could be easily to find meaningful information of the XML documents and analyze the information. Moreover, when a large volume of business data (XML documents) exists, one way to support the management of the XML documents is to apply the relational databases. For such an approach, we must transform the XML documents to the relational databases. In this thesis, we design and implement the indexing strategies to efficiently access XML documents. XML document is fundamentally different from relational data. XML is a hierarchical and nested document, it is very similar to the semistructured data model. The characteristic of semistructured data is that it may not have a fixed schema and it may be irregular or incomplete. Though, the semistructured data model is flexible in data modeling, it requires a large search space in query processing since there is no schema fixed in advance. Indexing is the way of how to improve query performance efficiently. However, due to the special properties of semistructued data, there are up to five types of queries: (1) complete single path, (2) specified leaf only, (3) specified intrapath, (4) specified attribute/element(value), and (5) multiple paths with the same level. In this thesis, we classify all possible queries into those five query types. Next, we create different indexes for different query types. Moreover, we design and implement the query transformation from XML query statements to SQL statements. Also, we create a user-friendly interface for users to input XML query statements. The whole system is implemented in JAVA and SQL Server 2000. From our experiences, we show that our indexing strategies can improve the XML query processing performance very well.