A 1.5 hrs UBC Library instructional workshop was presented by the UBC Science and Engineering librarians, Eugene Barsky and Kevin Lindstrom. Topics covered were: information on using Google, Google Scholar, and a comparison of Google/Google Scholar with Compendex (major engineering database).

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The importance of search partnerships has grown as a mode to search for innovations. However, in spite of this development, notions of open innovation combined with new propositions to change the search process in favour of sustainability have unravelled a need to take stock of the existing literature of search partnerships and the aims that these partnerships follow. This review addresses this shortcoming and synthesises the literature on search partnerships to analyse the current state of knowledge to deliver future research opportunities. A systematic review process was adopted by means of a set a set of pre-defined stages. These stages included the formulation and positioning of the review question within the larger literature domains, a systematic research process which included the adoption of search strings, relevance and quality appraisal criteria, as well as a stock-taking process of descriptive and thematic features, which followed the logic of prescriptive synthesis. This process led to a representative sample of 73 articles which were analysed subsequently. The tentative findings reveal that the literature is underpinned by a combination of theories linking to evolutionary or transaction-based understandings of search partnerships. Also, six conditions were found to drive search partnerships and when they are likely to form. Moreover five interventions were identified that relate to the use of search methods, boundary spanning activities, and the number, type and involvement levels with the partner. Finally search partnerships have been found to yield five outcomes: partnerships, and various types of innovations, higher social goals, as well as market knowledge. By combining contexts, interventions, and outcomes, research opportunities are identified that should inform future reviews, including the need for more research in sustainability-led search partnership contexts and a better understanding of search strategy configurations in relation interventions used and anticipated search partnership outcomes obtained.

This work presents a framework by which a massive multiagent organization can be controlled and modified without resorting to micromanagement and without needing advanced knowledge of potentially complex organizations. In addition to their designated duties, agents in the proposed framework perform some method of determining optimal traits such as configurations, plans, knowledge bases and so forth. Traits follow survival of the fittest rules in which more successful traits overpower less successful ones. Subproblem partitions develop emergently as successful solutions are disseminated to and aggregated by unsuccessful agents. Provisions are provided to allow the administrator to guide the search process by injecting solutions known to work for a particular agent. The performance of the framework is evaluated via comparison to individual state-space search.

abstract: Many web search improvements have been developed since the advent of the modern search engine, but one underrepresented area is the application of specific customizations to search results for educational web sites. In order to address this issue and improve the relevance of search results in automated learning environments, this work has integrated context-aware search principles with applications of preference based re-ranking and query modifications. This research investigates several aspects of context-aware search principles, specifically context-sensitive and preference based re-ranking of results which take user inputs as to their preferred content, and combines this with search query modifications which automatically search for a variety of modified terms based on the given search query, integrating these results into the overall re-ranking for the context. The result of this work is a novel web search algorithm which could be applied to any online learning environment attempting to collect relevant resources for learning about a given topic. The algorithm has been evaluated through user studies comparing traditional search results to the context-aware results returned through the algorithm for a given topic. These studies explore how this integration of methods could provide improved relevance in the search results returned when compared against other modern search engines. / Dissertation/Thesis / Masters Thesis Computer Science 2014

This study examines the impact of the real estate broker on the effectiveness of buyer search by focusing on the linkages between search intensity and the duration of search. How long a buyer searches depends on how sensitive the buyer is to within-period search costs and across-period, sequential search costs. High-income individuals and other homebuyers with high within-period search costs tend to search longer and less intensively. Buyers with high across-period search costs, such as out-of-town buyers, tend to search more intensively. Brokers, by reducing the opportunity costs of within-period search, increase buyer search intensity, which in turn reduces actual search time.

Social applications on the Web have appeared as communication spaces for sharing knowledge and information. In particular, social applications can be considered valuable information sources because information in the applications is not only easily accessible but also revealing in that the information accrues via interactions between people. In this work, we address methods for finding relevant information in social media applications that use unique properties of these applications. In particular, we focus on three unique structures in social media: hierarchical structure, conversational structure, and social structure. Hierarchical structures are used to organize information according to certain rules. Conversational structures are formed by interactions within communities such as replies. Social structures represent social relationships among community members. These structures are designed to organize information and encourage people to participate in discussions in social applications. Accordingly, contexts extracted from these structures can be used to improve the effectiveness of search in social media relative to representations based solely on text content. To exploit these structures in retrieval frameworks, we need to address three challenges as follows. First, we should discover each structure because it is often obscure. Second, we need to extract relevant contexts from each structure because not all the contexts in a structure are relevant for retrieval. Last, we should represent each context or their combinations in a representation framework so that they can be encoded as retrieval components such as documents. In this work, we introduce an effective representation framework for multiple contexts. We then discuss how to discover or define each structure and how to extract relevant contexts from the structure. Using the representation framework, these relevant contexts are integrated into retrieval algorithms. To demonstrate that these structures can improve search in social media, the retrieval models and frameworks incorporating these structures are evaluated through experiments using data collections gathered from a variety of social media applications. In addition, we address two minor challenges related to social media search. First, it is not always easy to find relevant information from relevant objects if the objects are large. Accordingly, we address identification of relevant substructures in such objects. Second, text reuse structures are important since these structures have the potential to affect various retrieval tasks. In this thesis, we introduce text reuse structures and analyze text reuse patterns in real social applications.

Visual search consists of locating a known target amongst a field of distractors. Often times, observers must search for more than one object at once. Eye movements were monitored in a series of visual search experiments examining search efficiency and how color is represented in order to guide search for multiple targets. The results demonstrated that observers were very color selective when searching for a single color. However, when searching for two colors at once, the degree of similarity between the two target colors had varying effects on fixation patterns. Search for two very similar colors was almost as efficient as search for a single color. As this similarity between the targets deceased, search efficiency suffered, resulting in more fixations on objects dissimilar to both targets. In terms of representation, the results suggest that the guiding template or templates prevailed throughout search, and were relatively unaffected by the objects encountered. Fixation patterns revealed that two similarly colored objects may be represented as a single, unitary range containing the target colors as well as the colors in between in color space. As the degree of similarity between the targets decreased, the two targets were more likely to be represented as discrete separate templates.

Graph search is used in many areas of computer science. It is well-known that the scalability of graph-search algorithms such as A* is limited by their memory requirements. In this dissertation, I describe three complementary strategies for reducing the memory requirements of graph-search algorithms, especially for multiple sequence alignment (a central problem in computational molecular biology). These search strategies dramatically increase the range and difficulty of multiple sequence alignment problems that can be solved. The first strategy uses a divide-and-conquer method of solution reconstruction, and one of my contributions is to show that when divide-and-conquer solution reconstruction is used, a layer-by-layer strategy for multiple sequence alignment is more memory-efficient than a bestirst strategy. The second strategy is a new approach to duplicate detection in external-memory graph search that involves partitioning the search graph based on an abstraction of the state space. For graphs with sufficient local structure, it allows graph-search algorithms to use external memory, such as disk storage, almost as efficiently as internal memory. The third strategy is a technique for reducing the memory requirements of sub-alignment search heuristics that are stored in lookup tables. It uses the start and goal states of a problem instance to restrict the region of the state space for which a table-based heuristic is needed, making it possible to store more accurate heuristic estimates in the same amount of memory. These three strategies dramatically improve the scalability of graph search not only for multiple sequence alignment, but for many other graph-search problems, and generalizations of these search strategies for other graph-search problems are discussed throughout the dissertation.

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