A Genetic Algorithm Assisted Hybrid Approach to Web Information Integration

Heterogeneity and interoperability of Web data sources represent the current key issue in Web information extraction and integration. Warehouse approach and virtual approach are the common approaches adopted to integrate heterogeneous Web data sources. However, few analytic model and cost model were developed to measure and assess the efficiency and effectiveness of either approach or a combination. Hence, a contingency model cannot be produced to assist the search engine to select and mix the warehouse method and the virtual method. In this study, we present a genetic algorithm assisted hybrid approach to aid the search engine to evaluate the cost and performance factors. We apply genetic algorithm technique to formulate a cost optimization model and compute and compare the cost of extraction and integration. The cost model is based on a collection and compilation of the property data of the query analysis and path expression of the involved Web data sources. Six property analyses are conducted and six evolution steps are created to formulate the genetic algorithm of optimization. Further, we conduct a preliminary experiment using 15 local and global Web bookstores to install and test the method. Our experimental results show that the cost optimization can be achieved with the genetic algorithm and factor analysis

Innovative Platform for Designing Hybrid Collaborative & Context-Aware Data Mining Scenarios

The process of knowledge discovery involves nowadays a major number of techniques. Context-Aware Data Mining (CADM) and Collaborative Data Mining (CDM) are some of the recent ones. the current research proposes a new hybrid and efficient tool to design prediction models called Scenarios Platform-Collaborative & Context-Aware Data Mining (SP-CCADM). Both CADM and CDM approaches are included in the new platform in a flexible manner; SP-CCADM allows the setting and testing of multiple configurable scenarios related to data mining at once. The introduced platform was successfully tested and validated on real life scenarios, providing better results than each standalone technique-CADM and CDM. Nevertheless, SP-CCADM was validated with various machine learning algorithms-k-Nearest Neighbour (k-NN), Deep Learning (DL), Gradient Boosted Trees (GBT) and Decision Trees (DT). SP-CCADM makes a step forward when confronting complex data, properly approaching data contexts and collaboration between data. Numerical experiments and statistics illustrate in detail the potential of the proposed platform.Comment: 15 figure

Uhvatite znanje na licu mjesta: ususret autonomnoj i prožimajućoj usluzi sadržajnog znanja

Knowledge must be acquired not only at the moment when it is presented, but also at the site where it is applied to. To guarantee the immediate acquisition of context-rich knowledge at anytime and anywhere, fullyautomated as well as pervasive capabilities must be considered together. This paper proposes a methodology to capture knowledge on the spot in an autonomous and pervasive manner by deploying the Smartphone as a sensor to monitor and gather dialogue-based knowledge and context data. Smart-ConKAS (SMARTphone-based CONtextual Knowledge Acquisition System), a prototype system, is implemented to validate the proposed concepts.Znanje se stječe ne samo u trenutku kada je predstavljeno nego i na mjestu gdje se primjenjuje. Kako bi se jamčilo trenutno stjecanje znanja bilo kada i bilo gdje moraju se uzeti u obzir potpuno automatizirane i prožimajuće sposobnosti. U ovom radu predložena je metoda stjecanja znanja na licu mjesta na autonoman i prožimajuć način korištenjem pametnog telefona kao senzora za nadgledanje i skupljanje znanja i podataka. Smart - ConKAS (SMARTphonebased CONtextual Knowledge Acquisition System) je prototip koji je korišten kako bi se potvrdio predloženi koncept

Self-adaptation via concurrent multi-action evaluation for unknown context

Context-aware computing has been attracting growing attention in recent years. Generally, there are several ways for a context-aware system to select a course of action for a particular change of context. One way is for the system developers to encompass all possible context changes in the domain knowledge. Other methods include system inferences and adaptive learning whereby the system executes one action and evaluates the outcome and self-adapts/self-learns based on that. However, in situations where a system encounters unknown contexts, the iterative approach would become unfeasible when the size of the action space increases. Providing efficient solutions to this problem has been the main goal of this research project. Based on the developed abstract model, the designed methodology replaces the single action implementation and evaluation by multiple actions implemented and evaluated concurrently. This parallel evaluation of actions speeds up significantly the evolution time taken to select the best action suited to unknown context compared to the iterative approach. The designed and implemented framework efficiently carries out concurrent multi-action evaluation when an unknown context is encountered and finds the best course of action. Two concrete implementations of the framework were carried out demonstrating the usability and adaptability of the framework across multiple domains. The first implementation was in the domain of database performance tuning. The concrete implementation of the framework demonstrated the ability of concurrent multi-action evaluation technique to performance tune a database when performance is regressed for an unknown reason. The second implementation demonstrated the ability of the framework to correctly determine the threshold price to be used in a name-your-own-price channel when an unknown context is encountered. In conclusion the research introduced a new paradigm of a self-adaptation technique for context-aware application. Among the existing body of work, the concurrent multi-action evaluation is classified under the abstract concept of experiment-based self-adaptation techniques

Using Unified Personal Information in Workspaces

Knowledge workers (KWers) deal with personal information and use tools like, e.g., desktop workspaces to support their work. But KWer support is hindered by personal information fragmentation, i.e., applications keep a set of personal information while not interconnecting it. This thesis addresses this in the domains personal task management and meeting management by using a common unified personal information model as offered by the semantic desktop personal information management (PIM) system

Challenges and Techniques for Personal Environment Management

People today use the computer for many simultaneous work projects and activities. The traditional file system was developed for storing and retrieving files and it and the desktop have not evolved with users' practices. The first part of the dissertation presents a user study that generates a better understanding of the issues and practices regarding the organization of documents in support of activities. The second part provides the design of an environment to organize information based on an activity paradigm as opposed to an archiving paradigm and delivers the instantiation and the evaluation of a system based on such a design. The system, called Docksy, provides an environment structured in workspaces. Each workspace is segmented in areas or panels. Users can use documents as elements to structure their workflow or to manage their activities by separating files in the different panels, and by adding comments, tags, and flags. The Docksy design aims to create a flexible, lightweight environment that is easy to use and can be incorporated into users' daily practice, old or new. Such a system could be used to learn about users' practices and their evolution. Docksy was therefore developed for a double purpose; the short term purpose of testing new features (panels, comments, flags, and tags) and the long term purpose of facilitating learning about user practices. A study of Docksy use was conducted in which twenty participants used Docksy for at least two weeks and they were then interviewed. The study showed that participants valued the panels and the comment features. The results of the study showed the potential for changing users' practice and the potential for the system to be adopted by users

Context-centered design: bridging the gap between designing and understanding

The design of Electronic Medical Record (EMR) systems and other clinical information systems challenges traditional HCI design in the following two ways: (a) system interactions involve multiple clinician/non-clinician teams, various interactive medical devices and medical artifacts in highly mobilized contexts, and (b) it is extremely difficult for designers to understand the highly knowledge-intensive clinical medicine field and design a system to fit into the hospital environment. Past literature suggests that context of system use could potentially solve these EMR design challenges. To enhance system design quality and bridge designers’ expertise and end-users’ domain understanding, we developed an operational method called Context-Centered Framework and carried out an empirical study to test the effectiveness of it. The empirical study examined the impact of the framework on a mobilized nursing task using scenario-based design and claims analysis approaches. The results indicated that designers improved their understanding towards the clinicians’ working environment and incorporated more usability concerns in their design product through the use of the Context-Centered Framework. This suggests that focusing on the context of system use could improve the quality of design for the systems situated in the highly complex, mobile and ubiquitous environment and could benefit clinicians’ practice.Ph.D., Information Studies -- Drexel University, 200