Integration and querying of heterogeneous, autonomous, distributed database systems

Today, databases have become an integral part of information systems. In the past two decades, we have seen different database systems being developed independently and used in different applications domains. Today's interconnected networks and advanced applications, such as data warehousing, data mining & knowledge discovery and intelligent data access to information on the Web, have created a need for integrated access to such heterogeneous, autonomous, distributed database systems. Heterogeneous/multidatabase research has focused on this issue resulting in many different approaches. However, a single, generally accepted methodology in academia or industry has not emerged providing ubiquitous intelligent data access from heterogeneous, autonomous, distributed information sources. ^ This thesis describes a heterogeneous database system being developed at High-performance Database Research Center (HPDRC). A major impediment to ubiquitous deployment of multidatabase technology is the difficulty in resolving semantic heterogeneity. That is, identifying related information sources for integration and querying purposes. Our approach considers the semantics of the meta-data constructs in resolving this issue. The major contributions of the thesis work include: (i) providing a scalable, easy-to-implement architecture for developing a heterogeneous multidatabase system, utilizing Semantic Binary Object-oriented Data Model (Sem-ODM) and Semantic SQL query language to capture the semantics of the data sources being integrated and to provide an easy-to-use query facility; (ii) a methodology for semantic heterogeneity resolution by investigating into the extents of the meta-data constructs of component schemas. This methodology is shown to be correct, complete and unambiguous; (iii) a semi-automated technique for identifying semantic relations, which is the basis of semantic knowledge for integration and querying, using shared ontologies for context-mediation; (iv) resolutions for schematic conflicts and a language for defining global views from a set of component Sem-ODM schemas; (v) design of a knowledge base for storing and manipulating meta-data and knowledge acquired during the integration process. This knowledge base acts as the interface between integration and query processing modules; (vi) techniques for Semantic SQL query processing and optimization based on semantic knowledge in a heterogeneous database environment; and (vii) a framework for intelligent computing and communication on the Internet applying the concepts of our work. ^

Integration and querying of heterogeneous, autonomous, distributed database systems

Today, databases have become an integral part of information systems. In the past two decades, we have seen different database systems being developed independently and used in different applications domains. Today's interconnected networks and advanced applications, such as data warehousing, data mining & knowledge discovery and intelligent data access to information on the Web, have created a need for integrated access to such heterogeneous, autonomous, distributed database systems. Heterogeneous/multidatabase research has focused on this issue resulting in many different approaches. However, a single, generally accepted methodology in academia or industry has not emerged providing ubiquitous intelligent data access from heterogeneous, autonomous, distributed information sources. This thesis describes a heterogeneous database system being developed at Highperformance Database Research Center (HPDRC). A major impediment to ubiquitous deployment of multidatabase technology is the difficulty in resolving semantic heterogeneity. That is, identifying related information sources for integration and querying purposes. Our approach considers the semantics of the meta-data constructs in resolving this issue. The major contributions of the thesis work include: (i.) providing a scalable, easy-to-implement architecture for developing a heterogeneous multidatabase system, utilizing Semantic Binary Object-oriented Data Model (Sem-ODM) and Semantic SQL query language to capture the semantics of the data sources being integrated and to provide an easy-to-use query facility; (ii.) a methodology for semantic heterogeneity resolution by investigating into the extents of the meta-data constructs of component schemas. This methodology is shown to be correct, complete and unambiguous; (iii.) a semi-automated technique for identifying semantic relations, which is the basis of semantic knowledge for integration and querying, using shared ontologies for context-mediation; (iv.) resolutions for schematic conflicts and a language for defining global views from a set of component Sem-ODM schemas; (v.) design of a knowledge base for storing and manipulating meta-data and knowledge acquired during the integration process. This knowledge base acts as the interface between integration and query processing modules; (vi.) techniques for Semantic SQL query processing and optimization based on semantic knowledge in a heterogeneous database environment; and (vii.) a framework for intelligent computing and communication on the Internet applying the concepts of our work.

Electrochemical Immunosensing of Cortisol in an Automated Microfluidic System Towards Point-of-Care Applications

This dissertation describes the development of a label-free, electrochemical immunosensing platform integrated into a low-cost microfluidic system for the sensitive, selective and accurate detection of cortisol, a steroid hormone co-related with many physiological disorders. Abnormal levels of cortisol is indicative of conditions such as Cushing’s syndrome, Addison’s disease, adrenal insufficiencies and more recently post-traumatic stress disorder (PTSD). Electrochemical detection of immuno-complex formation is utilized for the sensitive detection of Cortisol using Anti-Cortisol antibodies immobilized on sensing electrodes. Electrochemical detection techniques such as cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) have been utilized for the characterization and sensing of the label-free detection of Cortisol. The utilization of nanomaterial’s as the immobilizing matrix for Anti-cortisol antibodies that leads to improved sensor response has been explored. A hybrid nano-composite of Polyanaline-Ag/AgO film has been fabricated onto Au substrate using electrophoretic deposition for the preparation of electrochemical immunosening of cortisol. Using a conventional 3-electrode electrochemical cell, a linear sensing range of 1pM to 1µM at a sensitivity of 66µA/M and detection limit of 0.64pg/mL has been demonstrated for detection of cortisol. Alternately, a self-assembled monolayer (SAM) of dithiobis(succinimidylpropionte) (DTSP) has been fabricated for the modification of sensing electrode to immobilize with Anti-Cortisol antibodies. To increase the sensitivity at lower detection limit and to develop a point-of-care sensing platform, the DTSP-SAM has been fabricated on micromachined interdigitated microelectrodes (µIDE). Detection of cortisol is demonstrated at a sensitivity of 20.7µA/M and detection limit of 10pg/mL for a linear sensing range of 10pM to 200nM using the µIDE’s. A simple, low-cost microfluidic system is designed using low-temperature co-fired ceramics (LTCC) technology for the integration of the electrochemical cortisol immunosensor and automation of the immunoassay. For the first time, the non-specific adsorption of analyte on LTCC has been characterized for microfluidic applications. The design, fabrication technique and fluidic characterization of the immunoassay are presented. The DTSP-SAM based electrochemical immunosensor on µIDE is integrated into the LTCC microfluidic system and cortisol detection is achieved in the microfluidic system in a fully automated assay. The fully automated microfluidic immunosensor hold great promise for accurate, sensitive detection of cortisol in point-of-care applications.