The massive volumes and the huge variety of large knowledge bases make information exploration and analysis difficult. An important activity is data filtering and selection, in which both querying and visualization play important roles. Interfaces for data exploration environments normally include both, integrating them as tightly as possible. But many features of information exploration environments, such as visual representation of queries, visualization of query results, interactive data selection from visualizations, have only been studied separately. The intrinsic connections between them have not been described formally. The lack of formal descriptions inhibits the development of techniques that produce new representations for queries, and natural integration of visual query specification with query result visualization. This thesis describes a formalism that describes the basic components of information exploration and and their relationships in information exploration environments. The key aspect of the formalism is that it unifies querying and visualization within a single framework, which provides a foundation for designing and analysing visual query interfaces. Various innovative designs of visual query representations can be derived from the formalism. Simply comparing them with existing ones is not enough, it is more important to discover why one visual representation is better or worse than another. To do this it is necessary to understand users’ cognitive activities, and to know how these cognitive activities are enhanced or inhibited by different presentations of a query so that novel interfaces can be created and improved based on user testing. This thesis presents a new experimental methodology for evaluating query representations, which uses stimulus onset asynchrony to separate different aspects of query comprehension. This methodology was used to evaluate a new visual query representation based on Karnaugh maps, and showing that there are two qualitatively different approaches to comprehension: deductive and inductive. The Karnaugh map representation scales extremely well with query complexity, and the experiment shows that its good scaling properties occur because it strongly facilitates inductive comprehension.

The massive volumes and the huge variety of large knowledge bases make information exploration and analysis difficult. An important activity is data filtering and selection, in which both querying and visualization play important roles. Interfaces for data exploration environments normally include both, integrating them as tightly as possible. But many features of information exploration environments, such as visual representation of queries, visualization of query results, interactive data selection from visualizations, have only been studied separately. The intrinsic connections between them have not been described formally. The lack of formal descriptions inhibits the development of techniques that produce new representations for queries, and natural integration of visual query specification with query result visualization. This thesis describes a formalism that describes the basic components of information exploration and and their relationships in information exploration environments. The key aspect of the formalism is that it unifies querying and visualization within a single framework, which provides a foundation for designing and analysing visual query interfaces. Various innovative designs of visual query representations can be derived from the formalism. Simply comparing them with existing ones is not enough, it is more important to discover why one visual representation is better or worse than another. To do this it is necessary to understand users’ cognitive activities, and to know how these cognitive activities are enhanced or inhibited by different presentations of a query so that novel interfaces can be created and improved based on user testing. This thesis presents a new experimental methodology for evaluating query representations, which uses stimulus onset asynchrony to separate different aspects of query comprehension. This methodology was used to evaluate a new visual query representation based on Karnaugh maps, and showing that there are two qualitatively different approaches to comprehension: deductive and inductive. The Karnaugh map representation scales extremely well with query complexity, and the experiment shows that its good scaling properties occur because it strongly facilitates inductive comprehension.

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As the number of the published web services increase, discovery of the web services with the desired functionality and quality is becoming a challenging process. Selecting the appropriate web services among the ones that oer the same functionality is also a challenging task. The web service repositories like UDDI (Universal Description Discovery and Integration) support only the syntactic searchs. Quality of service parameters for the published web services can not be queried over these repositories. We have proposed a query language that aims to overcome these problems. It enables its users to query the web services both syntactically and semantically. We also allow the users to specify the quality of service criteria which the desired web services should satisfy. We have developed a graphical query interface to assist the users in query sentence formulation process. The proposed work is developed as a submodule of the Domain Specific Web Service Discovery with Semantics (DSWSD-S) System. Aforementioned query language and the query interface are explained in detail in this thesis.

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Traditional data management technologies originating from business domain are currently facing many challenges from other domains such as scientific research. Data structures in databases are becoming more and more complex and data query functions are moving from the back-end database level towards the front-end user-interface level. Traditional query languages such as SQL, OQL, and form-based query interfaces cannot fully meet the needs today. This research is motivated by the data management issues in life science applications. I propose a methodology for domain-specific conceptual data modeling and querying. The methodology can be applied to any domain to capture more domain semantics and empower end-users to formulate a query at the conceptual level with terminologies and functions familiar to them. The query system resulting from the methodology is designed to work on all major types of database management systems (DBMS) and support end-users to dynamically define and add new domain-specific functions. That is, all user-defined functions can be either pre-defined by domain experts and/or data model creators at the time of system creation, or dynamically defined by end-users from the client side at any time. The methodology has a domain-specific conceptual data model (DSC-DM) and a domain-specific conceptual query language (DSC-QL). DSC-QL uses only the abstract concepts, relationships, and functions defined in DSC-DM. It is a user-oriented high level query language and intentionally designed to be flexible, extensible, and readily usable. DSC-QL queries are much simpler than corresponding SQL or OQL queries because of advanced features such as user-defined functions, composite and set attributes, dot-path expressions, and super-classes. DSC-QL can be translated into SQL and OQL through a dynamic mapping function, and automatically updated when the underlying database schema evolves. The operational and declarative semantics of DSC-QL are formally defined in terms of graphs. A normal form for DSC-QL as a standard format for the mappings from flexible conceptual expressions to restricted SQL or OQL statements is also defined. Two translation algorithms from normalized DSC-QL to SQL and OQL are introduced. Through comparison, DSC-QL is shown to have very good balance between simplicity and expressive power and is suitable for end-users. Implementation details of the query system are reported as well. Two prototypes have been built. One prototype is for neuroscience domain, which is built on an object-oriented DBMS. The other one is for traditional business domain, which is built on a relational DBMS.