Utilising Intranet technologies in construction sector SMEs:building up knowledge bases for extreme weather event risk management

Intranet technologies accessible through a web based platform are used to share and build knowledge bases in many industries. Previous research suggests that intranets are capable of providing a useful means to share, collaborate and transact information within an organization. To compete and survive successfully, business organisations are required to effectively manage various risks affecting their businesses. In the construction industry too this is increasingly becoming an important element in business planning. The ability of businesses, especially of SMEs which represent a significant portion in most economies, to manage various risks is often hindered by fragmented knowledge across a large number of businesses. As a solution, this paper argues that Intranet technologies can be used as an effective means of building and sharing knowledge and building up effective knowledge bases for risk management in SMEs, by specifically considering the risks of extreme weather events. The paper discusses and evaluates relevant literature in this regard and identifies the potential for further research to explore this concept.

Using the Agglomerative Method of Hierarchical Clustering as a Data Mining Tool in Capital Market

The purpose of this paper is to explain the notion of clustering and a concrete clustering method- agglomerative hierarchical clustering algorithm. It shows how a data mining method like clustering can be applied to the analysis of stocks, traded on the Bulgarian Stock Exchange in order to identify similar temporal behavior of the traded stocks. This problem is solved with the aid of a data mining tool that is called XLMiner™ for Microsoft Excel Office.

Indirect ties in knowledge networks:a social network analysis with ordered weighted averaging operators

This PhD thesis analyses networks of knowledge flows, focusing on the role of indirect ties in the knowledge transfer, knowledge accumulation and knowledge creation process. It extends and improves existing methods for mapping networks of knowledge flows in two different applications and contributes to two stream of research. To support the underlying idea of this thesis, which is finding an alternative method to rank indirect network ties to shed a new light on the dynamics of knowledge transfer, we apply Ordered Weighted Averaging (OWA) to two different network contexts. Knowledge flows in patent citation networks and a company supply chain network are analysed using Social Network Analysis (SNA) and the OWA operator. The OWA is used here for the first time (i) to rank indirect citations in patent networks, providing new insight into their role in transferring knowledge among network nodes; and to analyse a long chain of patent generations along 13 years; (ii) to rank indirect relations in a company supply chain network, to shed light on the role of indirectly connected individuals involved in the knowledge transfer and creation processes and to contribute to the literature on knowledge management in a supply chain. In doing so, indirect ties are measured and their role as means of knowledge transfer is shown. Thus, this thesis represents a first attempt to bridge the OWA and SNA fields and to show that the two methods can be used together to enrich the understanding of the role of indirectly connected nodes in a network. More specifically, the OWA scores enrich our understanding of knowledge evolution over time within complex networks. Future research can show the usefulness of OWA operator in different complex networks, such as the on-line social networks that consists of thousand of nodes.

The Development of the Generalization Algorithm Based on the Rough Set Theory

This paper considers the problem of concept generalization in decision-making systems where such features of real-world databases as large size, incompleteness and inconsistence of the stored information are taken into account. The methods of the rough set theory (like lower and upper approximations, positive regions and reducts) are used for the solving of this problem. The new discretization algorithm of the continuous attributes is proposed. It essentially increases an overall performance of generalization algorithms and can be applied to processing of real value attributes in large data tables. Also the search algorithm of the significant attributes combined with a stage of discretization is developed. It allows avoiding splitting of continuous domains of insignificant attributes into intervals.

Knowledge Presentation and Reasoning with Loglinear Models

Our approach for knowledge presentation is based on the idea of expert system shell. At first we will build a graph shell of both possible dependencies and possible actions. Then, reasoning by means of Loglinear models, we will activate some nodes and some directed links. In this way a Bayesian network and networks presenting loglinear models are generated.

Knowledge acquisition and research evidence in autism:researcher and practitioner perspectives and engagement

Background: Government policy and national practice guidelines have created an increasing need for autism services to adopt an evidence-based practice approach. However, a gap continues to exist between research evidence and its application. This study investigated the difference between autism researchers and practitioners in their methods of acquiring knowledge. Methods: In a questionnaire study, 261 practitioners and 422 researchers reported on the methods they use and perceive to be beneficial for increasing research access and knowledge. They also reported on their level of engagement with members of the other professional community. Results: Researchers and practitioners reported different methods used to access information. Each group, however, had similar overall priorities regarding access to research information. While researchers endorsed the use of academic journals significantly more often than practitioners, both groups included academic journals in their top three choices. The groups differed in the levels of engagement they reported; researchers indicated they were more engaged with practitioners than vice versa. Conclusions: Comparison of researcher and practitioner preferences led to several recommendations to improve knowledge sharing and translation, including enhancing access to original research publications, facilitating informal networking opportunities and the development of proposals for the inclusion of practitioners throughout the research process.

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. ^

Scaling geospatial searches in large spatial databases

Modern geographical databases, which are at the core of geographic information systems (GIS), store a rich set of aspatial attributes in addition to geographic data. Typically, aspatial information comes in textual and numeric format. Retrieving information constrained on spatial and aspatial data from geodatabases provides GIS users the ability to perform more interesting spatial analyses, and for applications to support composite location-aware searches; for example, in a real estate database: “Find the nearest homes for sale to my current location that have backyard and whose prices are between $50,000 and $80,000”. Efficient processing of such queries require combined indexing strategies of multiple types of data. Existing spatial query engines commonly apply a two-filter approach (spatial filter followed by nonspatial filter, or viceversa), which can incur large performance overheads. On the other hand, more recently, the amount of geolocation data has grown rapidly in databases due in part to advances in geolocation technologies (e.g., GPS-enabled smartphones) that allow users to associate location data to objects or events. The latter poses potential data ingestion challenges of large data volumes for practical GIS databases. In this dissertation, we first show how indexing spatial data with R-trees (a typical data pre-processing task) can be scaled in MapReduce—a widely-adopted parallel programming model for data intensive problems. The evaluation of our algorithms in a Hadoop cluster showed close to linear scalability in building R-tree indexes. Subsequently, we develop efficient algorithms for processing spatial queries with aspatial conditions. Novel techniques for simultaneously indexing spatial with textual and numeric data are developed to that end. Experimental evaluations with real-world, large spatial datasets measured query response times within the sub-second range for most cases, and up to a few seconds for a small number of cases, which is reasonable for interactive applications. Overall, the previous results show that the MapReduce parallel model is suitable for indexing tasks in spatial databases, and the adequate combination of spatial and aspatial attribute indexes can attain acceptable response times for interactive spatial queries with constraints on aspatial data.

The civic achievement gap: A study on the civic knowledge, skills, and attitudes of Hispanic students in Miami-Dade County Public Schools

This study assessed the civic knowledge, skills, and attitudes of Hispanic eighth grade students in Miami-Dade County Public Schools (M-DCPS), Florida. Three hundred sixty one Hispanic students of Cuban (253), Colombian (57), and Nicaraguan (51) ancestry from 10 middle schools participated in the study. Two hundred twenty eight students were from low socio-economic status (SES) background, and 133 were of middle SES background. There were 136 boys and 225 girls. The International Association for the Evaluation of Educational Achievement Civic Education Student Questionnaire was used to collect data. The instrument assessed the students’ civic knowledge, skills, and attitudes. Multivariate analysis of variance was used to test for differences in the civic knowledge, skills, and attitudes of participants based on ancestry, SES, and gender. ^ The findings indicated that there was no significant difference in the civic knowledge, skills, and attitudes of Hispanic eighth grade students that were of Cuban, Colombian, and Nicaraguan ancestry. There was no significant difference in the civic skills and in five of the civic attitude scales for students from low SES families compared to those from middle SES families. However, there was a significant difference in the civic knowledge and in the civic attitude concerning classroom discussions and participation based on SES. The civic knowledge of middle SES students was higher than that of low SES students. Furthermore, middle SES Hispanic students displayed a higher mean score for the civic attitude of classroom discussions and participation than low SES students. There was no significant difference in the civic knowledge and in five of the civic attitude scales between boys and girls. However, there was a significant difference in the civic skills and the civic attitude of support for women’s rights between boys and girls. Hispanic girls displayed a higher mean score in civic skills than Hispanic boys. Furthermore, the mean score of civic attitude of support for women’s rights for Hispanic girls was higher than that of Hispanic boys. ^ It was concluded that Cuban, Colombian, or Nicaraguan participants did not demonstrate differences in civic attitudes and levels of civic knowledge and skills that eighth grade students possessed. In addition, when compared to boys, girls demonstrated a higher level of civic skills and a greater support for women’s rights and participation in politics and their roles in politics. Moreover, SES was demonstrated to be a key factor in the acquisition of civic knowledge, regardless of ancestry.^

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.

Sentiment-Topic Modelling in Text Mining

Peer reviewed

Sentiment-Topic Modelling in Text Mining

Peer reviewed

Sentiment-Topic Modelling in Text Mining

Peer reviewed

An Examination of Current Approaches to Integrative Indigenous and Western Knowledge System Implementation in Water Research and Management: A Case Study Encompassing the Colonized Geographies of Canada, Australia, New Zealand, and the United States

The commodification of natural resources and the pursuit of continuous growth has resulted in environmental degradation, depletion, and disparity in access to these life-sustaining resources, including water. Utility-based objectification and exploitation of water in some societies has brought us to the brink of crisis through an apathetic disregard for present and future generations. The ongoing depletion and degradation of the world’s water sources, coupled with a reliance on Western knowledge and the continued omission of Indigenous knowledge to manage our relationship with water has unduly burdened many, but particularly so for Indigenous communities. The goal of my thesis research is to call attention to and advance the value and validity of using both Indigenous and Western knowledge systems (also known as Two-Eyed Seeing) in water research and management to better care for water. To achieve this goal, I used a combined systematic and realist review method to identify and synthesize the peer-reviewed, integrative water literature, followed by semi-structured interviews with first authors of the exemplars from the included literature to identify the challenges and insights that researchers have experienced in conducting integrative water research. Findings suggest that these authors recognize that many previous attempts to integrate Indigenous knowledges have been tokenistic rather than meaningful, and that new methods for knowledge implementation are needed. Community-based participatory research methods, and the associated tenets of balancing power, fostering trust, and community ownership over the research process, emerged as a pathway towards the meaningful implementation of Indigenous and Western knowledge systems. Data also indicate that engagement and collaborative governance structures developed from a position of mutual respect are integral to the realization of a given project. The recommendations generated from these findings offer support for future Indigenous-led research and partnerships through the identification and examination of approaches that facilitate the meaningful implementation of Indigenous and Western knowledge systems in water research and management. Asking Western science questions and seeking Indigenous science solutions does not appear to be working; instead, the co-design of research projects and asking questions directed at the problem rather than the solution better lends itself to the strengths of Indigenous science.

In my ‘Mind’s Eye’: introspectionism, detectivism, and the basis of authoritative self-knowledge

It is widely accepted that knowledge of certain of one’s own mental states is authoritative in being epistemically more secure than knowledge of the mental states of others, and theories of self-knowledge have largely appealed to one or the other of two sources to explain this special epistemic status. The first, ‘detectivist’, position, appeals to an inner perception-like basis, whereas the second, ‘constitutivist’, one, appeals to the view that the special security awarded to certain self-knowledge is a conceptual matter. I argue that there is a fundamental class of cases of authoritative self-knowledge, ones in which subjects are consciously thinking about their current, conscious intentional states, that is best accounted for in terms of a theory that is,
broadly speaking, introspectionist and detectivist. The position developed has an intuitive plausibility that has inspired many who work in the Cartesian tradition, and the potential to yield a single treatment of the basis of authoritative self-knowledge for both intentional states and sensation states.