Effective knowledge integration in emergency response organizations

Natural and man-made disasters have gained attention at all levels of policy-making in recent years. Emergency management tasks are inherently complex and unpredictable, and often require coordination among multiple organizations across different levels and locations. Effectively managing various knowledge areas and the organizations involved has become a critical emergency management success factor. However, there is a general lack of understanding about how to describe and assess the complex nature of emergency management tasks and how knowledge integration can help managers improve emergency management task performance. ^ The purpose of this exploratory research was first, to understand how emergency management operations are impacted by tasks that are complex and inter-organizational and second, to investigate how knowledge integration as a particular knowledge management strategy can improve the efficiency and effectiveness of the emergency tasks. Three types of specific knowledge were considered: context-specific, technology-specific, and context-and-technology-specific. ^ The research setting was the Miami-Dade Emergency Operations Center (EOC) and the study was based on the survey responses from the participants in past EOC activations related to their emergency tasks and knowledge areas. The data included task attributes related to complexity, knowledge area, knowledge integration, specificity of knowledge, and task performance. The data was analyzed using multiple linear regressions and path analyses, to (1) examine the relationships between task complexity, knowledge integration, and performance, (2) the moderating effects of each type of specific knowledge on the relationship between task complexity and performance, and (3) the mediating role of knowledge integration. ^ As per theory-based propositions, the results indicated that overall component complexity and interactive complexity tend to have a negative effect on task performance. But surprisingly, procedural rigidity tended to have a positive effect on performance in emergency management tasks. Also as per our expectation, knowledge integration had a positive relationship with task performance. Interestingly, the moderating effects of each type of specific knowledge on the relationship between task complexity and performance were varied and the extent of mediation of knowledge integration depended on the dimension of task complexity. ^

Effective Knowledge Integration in Emergency Response Organizations

Natural and man-made disasters have gained attention at all levels of policy-making in recent years. Emergency management tasks are inherently complex and unpredictable, and often require coordination among multiple organizations across different levels and locations. Effectively managing various knowledge areas and the organizations involved has become a critical emergency management success factor. However, there is a general lack of understanding about how to describe and assess the complex nature of emergency management tasks and how knowledge integration can help managers improve emergency management task performance. The purpose of this exploratory research was first, to understand how emergency management operations are impacted by tasks that are complex and inter-organizational and second, to investigate how knowledge integration as a particular knowledge management strategy can improve the efficiency and effectiveness of the emergency tasks. Three types of specific knowledge were considered: context-specific, technology-specific, and context-and-technology-specific. The research setting was the Miami-Dade Emergency Operations Center (EOC) and the study was based on the survey responses from the participants in past EOC activations related to their emergency tasks and knowledge areas. The data included task attributes related to complexity, knowledge area, knowledge integration, specificity of knowledge, and task performance. The data was analyzed using multiple linear regressions and path analyses, to (1) examine the relationships between task complexity, knowledge integration, and performance, (2) the moderating effects of each type of specific knowledge on the relationship between task complexity and performance, and (3) the mediating role of knowledge integration. As per theory-based propositions, the results indicated that overall component complexity and interactive complexity tend to have a negative effect on task performance. But surprisingly, procedural rigidity tended to have a positive effect on performance in emergency management tasks. Also as per our expectation, knowledge integration had a positive relationship with task performance. Interestingly, the moderating effects of each type of specific knowledge on the relationship between task complexity and performance were varied and the extent of mediation of knowledge integration depended on the dimension of task complexity.

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. ^

Knowledge discovery using multiple sources of biological data

The primary aim of this dissertation is to develop data mining tools for knowledge discovery in biomedical data when multiple (homogeneous or heterogeneous) sources of data are available. The central hypothesis is that, when information from multiple sources of data are used appropriately and effectively, knowledge discovery can be better achieved than what is possible from only a single source. ^ Recent advances in high-throughput technology have enabled biomedical researchers to generate large volumes of diverse types of data on a genome-wide scale. These data include DNA sequences, gene expression measurements, and much more; they provide the motivation for building analysis tools to elucidate the modular organization of the cell. The challenges include efficiently and accurately extracting information from the multiple data sources; representing the information effectively, developing analytical tools, and interpreting the results in the context of the domain. ^ The first part considers the application of feature-level integration to design classifiers that discriminate between soil types. The machine learning tools, SVM and KNN, were used to successfully distinguish between several soil samples. ^ The second part considers clustering using multiple heterogeneous data sources. The resulting Multi-Source Clustering (MSC) algorithm was shown to have a better performance than clustering methods that use only a single data source or a simple feature-level integration of heterogeneous data sources. ^ The third part proposes a new approach to effectively incorporate incomplete data into clustering analysis. Adapted from K-means algorithm, the Generalized Constrained Clustering (GCC) algorithm makes use of incomplete data in the form of constraints to perform exploratory analysis. Novel approaches for extracting constraints were proposed. For sufficiently large constraint sets, the GCC algorithm outperformed the MSC algorithm. ^ The last part considers the problem of providing a theme-specific environment for mining multi-source biomedical data. The database called PlasmoTFBM, focusing on gene regulation of Plasmodium falciparum, contains diverse information and has a simple interface to allow biologists to explore the data. It provided a framework for comparing different analytical tools for predicting regulatory elements and for designing useful data mining tools. ^ The conclusion is that the experiments reported in this dissertation strongly support the central hypothesis.^

The effects of trained teachers' integration of dialogic reading discourse on Hispanic English Language Learners' literacy skills in kindergarten

This quasi-experimental Analysis of Covariance (ANCOVA) study explored whether the vocabulary and reading comprehension mean scores of Hispanic Kindergarten ELs whose teachers were trained to utilize Dialogic Reading (DR) discourse were higher than the mean scores of Hispanic ELs in kindergarten whose teachers were not trained to utilize DR discourse strategies. Sixty-three self-identified Hispanic, English Language Kindergarten students and four teachers participated in the study. The teachers were randomly assigned to either the experimental group (DR trained) or control group by drawing names from a hat. Student assignment to experimental versus comparison group was based on the teacher's assignment to either the experimental or comparison group. Thirty-one were assigned to the control group and 32 to the experimental group. The teachers were instructed to read the story to a group of six students (maximum) at a time, utilizing the DR discourse strategies they had been trained to implement. Subjects were read a story each week during the 8-week duration of the study. Teachers in the experimental group collaboratively selected 10 words each week from the Read Together Talk Together (RTTT) instructional stories that were utilized for vocabulary instruction. A test of homogeneity was conducted to evaluate whether the variance among the dependent variables was the same across the groups. An Analyses of Covariance (ANCOVA) was applied to analyze students' vocabulary and comprehension mean scores in the experimental group and the comparison group. The results of the study demonstrated a significant increase in the vocabulary and reading comprehension mean scores for the students whose teachers had been trained in DR discourse strategies. When comparing the two groups, the results revealed a statistically significant difference (p < 0.05). In conclusion, this study was conducted to explore how DR discourse may be an effective technique to teach literacy skills. The findings of this study showed that vocabulary knowledge and reading comprehension of Hispanic ELs were positively affected by the teachers' inclusion of dialogue during storybook reading. Its outcomes accentuated the need for teachers to provide assistance to ELs as they develop vocabulary knowledge and reading comprehension skills.

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.