Learning to select for information retrieval

The effective ranking of documents in search engines is based on various document features, such as the frequency of the query terms in each document, the length, or the authoritativeness of each document. In order to obtain a better retrieval performance, instead of using a single or a few features, there is a growing trend to create a ranking function by applying a learning to rank technique on a large set of features. Learning to rank techniques aim to generate an effective document ranking function by combining a large number of document features. Different ranking functions can be generated by using different learning to rank techniques or on different document feature sets. While the generated ranking function may be uniformly applied to all queries, several studies have shown that different ranking functions favour different queries, and that the retrieval performance can be significantly enhanced if an appropriate ranking function is selected for each individual query. This thesis proposes Learning to Select (LTS), a novel framework that selectively applies an appropriate ranking function on a per-query basis, regardless of the given query's type and the number of candidate ranking functions. In the learning to select framework, the effectiveness of a ranking function for an unseen query is estimated from the available neighbouring training queries. The proposed framework employs a classification technique (e.g. k-nearest neighbour) to identify neighbouring training queries for an unseen query by using a query feature. In particular, a divergence measure (e.g. Jensen-Shannon), which determines the extent to which a document ranking function alters the scores of an initial ranking of documents for a given query, is proposed for use as a query feature. The ranking function which performs the best on the identified training query set is then chosen for the unseen query. The proposed framework is thoroughly evaluated on two different TREC retrieval tasks (namely, Web search and adhoc search tasks) and on two large standard LETOR feature sets, which contain as many as 64 document features, deriving conclusions concerning the key components of LTS, namely the query feature and the identification of neighbouring queries components. Two different types of experiments are conducted. The first one is to select an appropriate ranking function from a number of candidate ranking functions. The second one is to select multiple appropriate document features from a number of candidate document features, for building a ranking function. Experimental results show that our proposed LTS framework is effective in both selecting an appropriate ranking function and selecting multiple appropriate document features, on a per-query basis. In addition, the retrieval performance is further enhanced when increasing the number of candidates, suggesting the robustness of the learning to select framework. This thesis also demonstrates how the LTS framework can be deployed to other search applications. These applications include the selective integration of a query independent feature into a document weighting scheme (e.g. BM25), the selective estimation of the relative importance of different query aspects in a search diversification task (the goal of the task is to retrieve a ranked list of documents that provides a maximum coverage for a given query, while avoiding excessive redundancy), and the selective application of an appropriate resource for expanding and enriching a given query for document search within an enterprise. The effectiveness of the LTS framework is observed across these search applications, and on different collections, including a large scale Web collection that contains over 50 million documents. This suggests the generality of the proposed learning to select framework. The main contributions of this thesis are the introduction of the LTS framework and the proposed use of divergence measures as query features for identifying similar queries. In addition, this thesis draws insights from a large set of experiments, involving four different standard collections, four different search tasks and large document feature sets. This illustrates the effectiveness, robustness and generality of the LTS framework in tackling various retrieval applications

Query Expansion Techniques for Enterprise Search

Although web search remains an active research area, interest in enterprise search has waned. This is despite the fact that the market for enterprise search applications is expected to triple within the next six years, and that knowledge workers spend an average of 1.6 to 2.5 hours each day searching for information. To improve search relevancy, and hence reduce this time, an enterprise- focused application must be able to handle the unique queries and constraints of the enterprise environment. The goal of this thesis research was to develop, implement, and study query expansion techniques that are most effective at improving relevancy in enterprise search. The case-study instrument used in this investigation was a custom Apache Solr-based search application deployed at a local medium-sized manufacturing company. It was hypothesized that techniques specifically tailored to the enterprise search environment would prove most effective. Query expansion techniques leveraging entity recognition, alphanumeric term identification, intent classification, collection enrichment, and word vectors were implemented and studied using real enterprise data. They were evaluated against a test set of queries developed using relevance survey results from multiple users, using standard relevancy metrics such as normalized discounted cumulative gain (nDCG). Comprehensive analysis revealed that the current implementation of the collection enrichment and word vector query expansion modules did not demonstrate meaningful improvements over the baseline methods. However, the entity recognition, alphanumeric term identification, and query intent classification modules produced meaningful and statistically significant improvements in relevancy, allowing us to accept the hypothesis

Methodology for the Implementation of Knowledge Management Systems 2.0

Web 2.0 and Big Data tools can be used to develop knowledge management systems based on facilitating the participation and collaboration of people in order to enhance knowledge. The paper presents a methodology that can help organizations with the use of Web 2.0 and Big Data tools to discover, gather, manage and apply their knowledge by making the process of implementing a knowledge management system faster and simpler. First, an initial version of the methodology was developed and it was then applied to an oil and gas company in order to analyze and refine it. The results obtained show the effectiveness of the methodology, since it helped this company to carry out the implementation quickly and effectively, thereby allowing the company to gain the maximum benefits from existing knowledge

The Impact of Organizational Changes on Increasing SMEs Competitiveness

Nowadays changes are compulsory for an organization in order to survive and stay competitive in a market. This paper discusses the aspects of understanding the general framework for an effective and efficient implementation of the organizational changes, as well as, their impact on motivation, employment, responsibility, competitive abilities, and it compares the measurable units of the capacity development for organizational changes. Besides, it focuses on creative dimensions and change management, new organizational knowledge, reward systems, managerial behavior, and organizational culture as a result of the organizational changes. Also, this study argues the value of organizational culture that needs to be shared between organization employees in order to help perform their duties as an important unit of the organizational change success. To obtain the results of this study are processed data of 200 SMEs that operate their activity in the Republic of Kosovo. These data were processed mainly using IBM SPSS software. The findings indicate that the proper organizational change management may help SMEs to be more successful in relation to the competition. And that the biggest challenge that successful managers face is to lead continuously the organizational systems toward the highest stages of organizational development. Moreover, results have shown that changes in the factors such as: organizational dimensions and organizational characteristics are closely related to competitive capability and organizational culture of the SMEs, as well as, changes in organizational culture may increase the competitive capability of SMEs in the competitive market.&nbsp

Factors Affecting Adoption of Enterprise Resource Planning Systems in the Kenyan Sugar Industry (A Case of Mumias Sugar Company LTD)

It was found necessary to undertake this study so as to bridge the knowledge gap as concerns the factors that affect the adoption of Enterprise Resource Planning systems. The specific objectives of the study were: to establish the role of Perceived Ease of Use of Enterprise Resource Planning in its adoption; to evaluate the effect of Perceived Usefulness of Enterprise Resource Planning on its adoption; and to determine the effects of individual perception towards the adoption of Enterprise Resource Planning. The study focused on Mumias Sugar Company. The population of interest was the employees of the company who are current users of the Systems Applications Product Enterprise Resource Planning, drawn from the three categories of staff as presented in the organizational structure - Heads of departments, Managers and Supervisors, whose number stood at 200 as at 31st December 2009. A semi-structured questionnaire was the main data collection instrument. The researcher also used interview schedules with open questions, aimed at meeting the objectives of the study. Primary data were analyzed by employing descriptive statistics such as percentages. The responses indicate that perceived ease of use was a key factor in determining adoption of Enterprise Resource Planning in Mumias Sugar Company. The employees embraced change of technology with anticipation for better performance, which further enhanced the adoption of Systems Applications Product Enterprise Resource Planning in the company. In line with perceived ease of use, the other factors that influenced the adoption of Systems Applications Product Enterprise Resource Planning in Mumias Sugar Company include the perceived feeling of comfort when using Systems Applications Product Enterprise Resource Planning, the user friendliness of Systems Applications Product Enterprise Resource Planning, the speed with which Systems Applications Product Enterprise Resource Planning processed transactions and the ability of the users to get support when using Systems Applications Product Enterprise Resource Planning. The findings also show that Perceived Usefulness is an important factor in determining the adaptation of innovations. The higher the perceived usefulness of the Enterprise Resource Planning system, the higher the chances that it would be adopted. Moreover, the degree to which an individual believes that using a particular system would enhance his or her job performance enhances the chances of adopting the system and the more the suitable the system is to the work ethic of the users the higher the acceptance rate. Further, the findings show that attitudes are a significant predictor of behavior. In addition, though individual attitude is necessary in determining adoption of new technologies, it is not sufficient condition for success. Certainly attitude may not strongly determine the intentions of an individual at the workplace regarding performance when additional factors e.g. usefulness are taken into account independently. Keywords: Enterprise Resource Planning, Perceived Ease of Use, Systems Applications Product, Legacy Systems, Perceived Usefulness, Material Requirements Planning, Business Processe

Integrated Space Asset Management Database and Modeling

Effective Space Asset Management is one key to addressing the ever-growing issue of space congestion. It is imperative that agencies around the world have access to data regarding the numerous active assets and pieces of space junk currently tracked in orbit around the Earth. At the center of this issues is the effective management of data of many types related to orbiting objects. As the population of tracked objects grows, so too should the data management structure used to catalog technical specifications, orbital information, and metadata related to those populations. Marshall Space Flight Center's Space Asset Management Database (SAM-D) was implemented in order to effectively catalog a broad set of data related to known objects in space by ingesting information from a variety of database and processing that data into useful technical information. Using the universal NORAD number as a unique identifier, the SAM-D processes two-line element data into orbital characteristics and cross-references this technical data with metadata related to functional status, country of ownership, and application category. The SAM-D began as an Excel spreadsheet and was later upgraded to an Access database. While SAM-D performs its task very well, it is limited by its current platform and is not available outside of the local user base. Further, while modeling and simulation can be powerful tools to exploit the information contained in SAM-D, the current system does not allow proper integration options for combining the data with both legacy and new M&S tools. This paper provides a summary of SAM-D development efforts to date and outlines a proposed data management infrastructure that extends SAM-D to support the larger data sets to be generated. A service-oriented architecture model using an information sharing platform named SIMON will allow it to easily expand to incorporate new capabilities, including advanced analytics, M&S tools, fusion techniques and user interface for visualizations. In addition, tight control of information sharing policy will increase confidence in the system, which would encourage industry partners to provide commercial data. Combined with the integration of new and legacy M&S tools, a SIMON-based architecture will provide a robust environment that can be extended and expanded indefinitely

The best of both worlds: highlighting the synergies of combining manual and automatic knowledge organization methods to improve information search and discovery.

Research suggests organizations across all sectors waste a significant amount of time looking for information and often fail to leverage the information they have. In response, many organizations have deployed some form of enterprise search to improve the 'findability' of information. Debates persist as to whether thesauri and manual indexing or automated machine learning techniques should be used to enhance discovery of information. In addition, the extent to which a knowledge organization system (KOS) enhances discoveries or indeed blinds us to new ones remains a moot point. The oil and gas industry was used as a case study using a representative organization. Drawing on prior research, a theoretical model is presented which aims to overcome the shortcomings of each approach. This synergistic model could help to re-conceptualize the 'manual' versus 'automatic' debate in many enterprises, accommodating a broader range of information needs. This may enable enterprises to develop more effective information and knowledge management strategies and ease the tension between what arc often perceived as mutually exclusive competing approaches. Certain aspects of the theoretical model may be transferable to other industries, which is an area for further research

Exploring Strategies to Integrate Disparate Bioinformatics Datasets

Distinct bioinformatics datasets make it challenging for bioinformatics specialists to locate the required datasets and unify their format for result extraction. The purpose of this single case study was to explore strategies to integrate distinct bioinformatics datasets. The technology acceptance model was used as the conceptual framework to understand the perceived usefulness and ease of use of integrating bioinformatics datasets. The population of this study included bioinformatics specialists of a research institution in Lebanon that has strategies to integrate distinct bioinformatics datasets. The data collection process included interviews with 6 bioinformatics specialists and reviewing 27 organizational documents relating to integrating bioinformatics datasets. Thematic analysis was used to identify codes and themes related to integrating distinct bioinformatics datasets. Key themes resulting from data analysis included a focus on integrating bioinformatics datasets, adding metadata with the submitted bioinformatics datasets, centralized bioinformatics database, resources, and bioinformatics tools. I showed throughout analyzing the findings of this study that specialists who promote standardizing techniques, adding metadata, and centralization may increase efficiency in integrating distinct bioinformatics datasets. Bioinformaticians, bioinformatics providers, the health care field, and society might benefit from this research. Improvement in bioinformatics affects poistevely the health-care field which has a positive social change. The results of this study might also lead to positive social change in research institutions, such as reduced workload, less frustration, reduction in costs, and increased efficiency while integrating distinct bioinformatics datasets

Cultivating Empathy: New Perspectives on Educating Business Leaders

Beyond rules, procedures, and manuals lie relationships. Jettisoning a formal hierarchical company structure allows all levels of management and employees to positively interact – this is where the key driver of “empathy” is so critical to continue building these relationships and molding a common organizational purpose

Understanding the lived experiences of Joint Honours graduates: how can educators best enable student success?

Combined or joint honours degrees represent 10% of all UK undergraduates. 50,000 out of 500,000 currently enrolled on all honours degrees. This significant and special way of learning therefore warrants scrutiny. This type of degree facilitates students combining the study of two subjects to honours level, with modules delivered from two academic disciplines. The large proportion of students on such degrees across universities in England and Wales means that debate is needed as to the intrinsic value of such degrees especially in relation to graduate employability and career opportunities. This paper examines the lived experiences of joint honours graduates, evaluating the impact that studying joint honours had on their careers, and whether they were well prepared for graduate roles. We draw out themes and characteristics that will assist educators in supporting their students and enabling their future success