User-centric music information retrieval

The rapid growth of the Internet and the advancements of the Web technologies have made it possible for users to have access to large amounts of on-line music data, including music acoustic signals, lyrics, style/mood labels, and user-assigned tags. The progress has made music listening more fun, but has raised an issue of how to organize this data, and more generally, how computer programs can assist users in their music experience. An important subject in computer-aided music listening is music retrieval, i.e., the issue of efficiently helping users in locating the music they are looking for. Traditionally, songs were organized in a hierarchical structure such as genre->artist->album->track, to facilitate the users’ navigation. However, the intentions of the users are often hard to be captured in such a simply organized structure. The users may want to listen to music of a particular mood, style or topic; and/or any songs similar to some given music samples. This motivated us to work on user-centric music retrieval system to improve users’ satisfaction with the system. The traditional music information retrieval research was mainly concerned with classification, clustering, identification, and similarity search of acoustic data of music by way of feature extraction algorithms and machine learning techniques. More recently the music information retrieval research has focused on utilizing other types of data, such as lyrics, user-access patterns, and user-defined tags, and on targeting non-genre categories for classification, such as mood labels and styles. This dissertation focused on investigating and developing effective data mining techniques for (1) organizing and annotating music data with styles, moods and user-assigned tags; (2) performing effective analysis of music data with features from diverse information sources; and (3) recommending music songs to the users utilizing both content features and user access patterns.

Document understanding using data mining and machine learning techniques

With the explosive growth of the volume and complexity of document data (e.g., news, blogs, web pages), it has become a necessity to semantically understand documents and deliver meaningful information to users. Areas dealing with these problems are crossing data mining, information retrieval, and machine learning. For example, document clustering and summarization are two fundamental techniques for understanding document data and have attracted much attention in recent years. Given a collection of documents, document clustering aims to partition them into different groups to provide efficient document browsing and navigation mechanisms. One unrevealed area in document clustering is that how to generate meaningful interpretation for the each document cluster resulted from the clustering process. Document summarization is another effective technique for document understanding, which generates a summary by selecting sentences that deliver the major or topic-relevant information in the original documents. How to improve the automatic summarization performance and apply it to newly emerging problems are two valuable research directions. To assist people to capture the semantics of documents effectively and efficiently, the dissertation focuses on developing effective data mining and machine learning algorithms and systems for (1) integrating document clustering and summarization to obtain meaningful document clusters with summarized interpretation, (2) improving document summarization performance and building document understanding systems to solve real-world applications, and (3) summarizing the differences and evolution of multiple document sources.

Mining the Online Social Network Data: Influence, Summarization, and Organization

Online Social Network (OSN) services provided by Internet companies bring people together to chat, share the information, and enjoy the information. Meanwhile, huge amounts of data are generated by those services (they can be regarded as the social media ) every day, every hour, even every minute, and every second. Currently, researchers are interested in analyzing the OSN data, extracting interesting patterns from it, and applying those patterns to real-world applications. However, due to the large-scale property of the OSN data, it is difficult to effectively analyze it. This dissertation focuses on applying data mining and information retrieval techniques to mine two key components in the social media data — users and user-generated contents. Specifically, it aims at addressing three problems related to the social media users and contents: (1) how does one organize the users and the contents? (2) how does one summarize the textual contents so that users do not have to go over every post to capture the general idea? (3) how does one identify the influential users in the social media to benefit other applications, e.g., Marketing Campaign? The contribution of this dissertation is briefly summarized as follows. (1) It provides a comprehensive and versatile data mining framework to analyze the users and user-generated contents from the social media. (2) It designs a hierarchical co-clustering algorithm to organize the users and contents. (3) It proposes multi-document summarization methods to extract core information from the social network contents. (4) It introduces three important dimensions of social influence, and a dynamic influence model for identifying influential users.

Information Discovery on Electronic Health Records Using Authority Flow Techniques

Background As the use of electronic health records (EHRs) becomes more widespread, so does the need to search and provide effective information discovery within them. Querying by keyword has emerged as one of the most effective paradigms for searching. Most work in this area is based on traditional Information Retrieval (IR) techniques, where each document is compared individually against the query. We compare the effectiveness of two fundamentally different techniques for keyword search of EHRs. Methods We built two ranking systems. The traditional BM25 system exploits the EHRs' content without regard to association among entities within. The Clinical ObjectRank (CO) system exploits the entities' associations in EHRs using an authority-flow algorithm to discover the most relevant entities. BM25 and CO were deployed on an EHR dataset of the cardiovascular division of Miami Children's Hospital. Using sequences of keywords as queries, sensitivity and specificity were measured by two physicians for a set of 11 queries related to congenital cardiac disease. Results Our pilot evaluation showed that CO outperforms BM25 in terms of sensitivity (65% vs. 38%) by 71% on average, while maintaining the specificity (64% vs. 61%). The evaluation was done by two physicians. Conclusions Authority-flow techniques can greatly improve the detection of relevant information in EHRs and hence deserve further study.

Dynamic data retrieval on the World Wide Web

Methods for accessing data on the Web have been the focus of active research over the past few years. In this thesis we propose a method for representing Web sites as data sources. We designed a Data Extractor data retrieval solution that allows us to define queries to Web sites and process resulting data sets. Data Extractor is being integrated into the MSemODB heterogeneous database management system. With its help database queries can be distributed over both local and Web data sources within MSemODB framework. ^ Data Extractor treats Web sites as data sources, controlling query execution and data retrieval. It works as an intermediary between the applications and the sites. Data Extractor utilizes a twofold “custom wrapper” approach for information retrieval. Wrappers for the majority of sites are easily built using a powerful and expressive scripting language, while complex cases are processed using Java-based wrappers that utilize specially designed library of data retrieval, parsing and Web access routines. In addition to wrapper development we thoroughly investigate issues associated with Web site selection, analysis and processing. ^ Data Extractor is designed to act as a data retrieval server, as well as an embedded data retrieval solution. We also use it to create mobile agents that are shipped over the Internet to the client's computer to perform data retrieval on behalf of the user. This approach allows Data Extractor to distribute and scale well. ^ This study confirms feasibility of building custom wrappers for Web sites. This approach provides accuracy of data retrieval, and power and flexibility in handling of complex cases. ^

Mining the online social network data: Influence, summarization, and organization

Online Social Network (OSN) services provided by Internet companies bring people together to chat, share the information, and enjoy the information. Meanwhile, huge amounts of data are generated by those services (they can be regarded as the social media ) every day, every hour, even every minute, and every second. Currently, researchers are interested in analyzing the OSN data, extracting interesting patterns from it, and applying those patterns to real-world applications. However, due to the large-scale property of the OSN data, it is difficult to effectively analyze it. This dissertation focuses on applying data mining and information retrieval techniques to mine two key components in the social media data — users and user-generated contents. Specifically, it aims at addressing three problems related to the social media users and contents: (1) how does one organize the users and the contents? (2) how does one summarize the textual contents so that users do not have to go over every post to capture the general idea? (3) how does one identify the influential users in the social media to benefit other applications, e.g., Marketing Campaign? The contribution of this dissertation is briefly summarized as follows. (1) It provides a comprehensive and versatile data mining framework to analyze the users and user-generated contents from the social media. (2) It designs a hierarchical co-clustering algorithm to organize the users and contents. (3) It proposes multi-document summarization methods to extract core information from the social network contents. (4) It introduces three important dimensions of social influence, and a dynamic influence model for identifying influential users.

Dynamic data retrieval on the world wide web

Methods for accessing data on the Web have been the focus of active research over the past few years. In this thesis we propose a method for representing Web sites as data sources. We designed a Data Extractor data retrieval solution that allows us to define queries to Web sites and process resulting data sets. Data Extractor is being integrated into the MSemODB heterogeneous database management system. With its help database queries can be distributed over both local and Web data sources within MSemODB framework. Data Extractor treats Web sites as data sources, controlling query execution and data retrieval. It works as an intermediary between the applications and the sites. Data Extractor utilizes a two-fold "custom wrapper" approach for information retrieval. Wrappers for the majority of sites are easily built using a powerful and expressive scripting language, while complex cases are processed using Java-based wrappers that utilize specially designed library of data retrieval, parsing and Web access routines. In addition to wrapper development we thoroughly investigate issues associated with Web site selection, analysis and processing. Data Extractor is designed to act as a data retrieval server, as well as an embedded data retrieval solution. We also use it to create mobile agents that are shipped over the Internet to the client's computer to perform data retrieval on behalf of the user. This approach allows Data Extractor to distribute and scale well. This study confirms feasibility of building custom wrappers for Web sites. This approach provides accuracy of data retrieval, and power and flexibility in handling of complex cases.

Text Analytics of Social Media: Sentiment Analysis, Event Detection and Summarization

In the last decade, large numbers of social media services have emerged and been widely used in people's daily life as important information sharing and acquisition tools. With a substantial amount of user-contributed text data on social media, it becomes a necessity to develop methods and tools for text analysis for this emerging data, in order to better utilize it to deliver meaningful information to users. Previous work on text analytics in last several decades is mainly focused on traditional types of text like emails, news and academic literatures, and several critical issues to text data on social media have not been well explored: 1) how to detect sentiment from text on social media; 2) how to make use of social media's real-time nature; 3) how to address information overload for flexible information needs. In this dissertation, we focus on these three problems. First, to detect sentiment of text on social media, we propose a non-negative matrix tri-factorization (tri-NMF) based dual active supervision method to minimize human labeling efforts for the new type of data. Second, to make use of social media's real-time nature, we propose approaches to detect events from text streams on social media. Third, to address information overload for flexible information needs, we propose two summarization framework, dominating set based summarization framework and learning-to-rank based summarization framework. The dominating set based summarization framework can be applied for different types of summarization problems, while the learning-to-rank based summarization framework helps utilize the existing training data to guild the new summarization tasks. In addition, we integrate these techneques in an application study of event summarization for sports games as an example of how to better utilize social media data.

What does board capital really bring to the table? Exploring the effect of directors' human and social capital on effective governance during international expansion

What constitutes effective corporate governance? Which director characteristics render boards effective at positively influencing firm-level performance outcomes? This dissertation examines these questions by taking a multilevel, multidisciplinary approach to corporate governance. I explore the individual-, team-, and firm- level factors that enable directors to serve effectively as strategic resources during international expansion. I argue that directors' international experience improves their ability to serve as effective strategic consultants and resource providers to firms during the complex internationalization process. However, unlike prior research, which tends to assume that directors with the potential to provide important resources uniformly do so, I acknowledge contextual factors (i.e. board cohesiveness, strategic relevance of directors' experience) that affect their propensity to actually influence outcomes. I explore these issues in three essays: one review essay and two empirical essays.^ In the first empirical essay, I integrate resource dependence theory with insights from social-psychological research to explore the influence of board capital on firms' cross-border M&A performance. Using a sample of cross-border M&As completed by S&P 500 firms from 2004-2009, I find evidence that directors' depth of international experience is associated with superior pre-deal outcomes. This suggests that boards' deep, market-specific knowledge is valuable during the target selection phase. I further find that directors' breadth of international experience is associated with superior post-deal performance, suggesting that these directors' global mindset helps firms in the post-M&A integration phase. I also find that these relationships are positively moderated by board cohesiveness, measured by boards' internal social ties.^ In the second empirical essay, I explore the boundary conditions of international board capital by examining how the characteristics of firms' internationalization strategy moderate the relationship between board capital and firm performance. Using a panel of 377 S&P 500 firms observed from 2004-2011, I find that boards' depth of international experience and social capital are more important during early stages of internationalization, when firms tend to lack market knowledge and legitimacy in the host markets. On the other hand, I find that breadth of international experience has a stronger relationship with performance when firms' have higher scope of internationalization, when information-processing demands are higher.^

Efficient Storage and Domain-Specific Information Discovery on Semistructured Documents

The increasing amount of available semistructured data demands efficient mechanisms to store, process, and search an enormous corpus of data to encourage its global adoption. Current techniques to store semistructured documents either map them to relational databases, or use a combination of flat files and indexes. These two approaches result in a mismatch between the tree-structure of semistructured data and the access characteristics of the underlying storage devices. Furthermore, the inefficiency of XML parsing methods has slowed down the large-scale adoption of XML into actual system implementations. The recent development of lazy parsing techniques is a major step towards improving this situation, but lazy parsers still have significant drawbacks that undermine the massive adoption of XML. Once the processing (storage and parsing) issues for semistructured data have been addressed, another key challenge to leverage semistructured data is to perform effective information discovery on such data. Previous works have addressed this problem in a generic (i.e. domain independent) way, but this process can be improved if knowledge about the specific domain is taken into consideration. This dissertation had two general goals: The first goal was to devise novel techniques to efficiently store and process semistructured documents. This goal had two specific aims: We proposed a method for storing semistructured documents that maps the physical characteristics of the documents to the geometrical layout of hard drives. We developed a Double-Lazy Parser for semistructured documents which introduces lazy behavior in both the pre-parsing and progressive parsing phases of the standard Document Object Model's parsing mechanism. The second goal was to construct a user-friendly and efficient engine for performing Information Discovery over domain-specific semistructured documents. This goal also had two aims: We presented a framework that exploits the domain-specific knowledge to improve the quality of the information discovery process by incorporating domain ontologies. We also proposed meaningful evaluation metrics to compare the results of search systems over semistructured documents.

What Does Board Capital Really Bring to the Table? Exploring the Effect of Directors’ Human and Social Capital on Effective Governance During International Expansion

What constitutes effective corporate governance? Which director characteristics render boards effective at positively influencing firm-level performance outcomes? This dissertation examines these questions by taking a multilevel, multidisciplinary approach to corporate governance. I explore the individual-, team-, and firm- level factors that enable directors to serve effectively as strategic resources during international expansion. I argue that directors’ international experience improves their ability to serve as effective strategic consultants and resource providers to firms during the complex internationalization process. However, unlike prior research, which tends to assume that directors with the potential to provide important resources uniformly do so, I acknowledge contextual factors (i.e. board cohesiveness, strategic relevance of directors’ experience) that affect their propensity to actually influence outcomes. I explore these issues in three essays: one review essay and two empirical essays. In the first empirical essay, I integrate resource dependence theory with insights from social-psychological research to explore the influence of board capital on firms’ cross-border M&A performance. Using a sample of cross-border M&As completed by S&P 500 firms from 2004-2009, I find evidence that directors’ depth of international experience is associated with superior pre-deal outcomes. This suggests that boards’ deep, market-specific knowledge is valuable during the target selection phase. I further find that directors’ breadth of international experience is associated with superior post-deal performance, suggesting that these directors’ global mindset helps firms in the post-M&A integration phase. I also find that these relationships are positively moderated by board cohesiveness, measured by boards’ internal social ties. In the second empirical essay, I explore the boundary conditions of international board capital by examining how the characteristics of firms’ internationalization strategy moderate the relationship between board capital and firm performance. Using a panel of 377 S&P 500 firms observed from 2004-2011, I find that boards’ depth of international experience and social capital are more important during early stages of internationalization, when firms tend to lack market knowledge and legitimacy in the host markets. On the other hand, I find that breadth of international experience has a stronger relationship with performance when firms’ have higher scope of internationalization, when information-processing demands are higher.

A generalized multidimensional index structure for multimedia data to support content-based similarity searches in a collaborative search environment

Since multimedia data, such as images and videos, are way more expressive and informative than ordinary text-based data, people find it more attractive to communicate and express with them. Additionally, with the rising popularity of social networking tools such as Facebook and Twitter, multimedia information retrieval can no longer be considered a solitary task. Rather, people constantly collaborate with one another while searching and retrieving information. But the very cause of the popularity of multimedia data, the huge and different types of information a single data object can carry, makes their management a challenging task. Multimedia data is commonly represented as multidimensional feature vectors and carry high-level semantic information. These two characteristics make them very different from traditional alpha-numeric data. Thus, to try to manage them with frameworks and rationales designed for primitive alpha-numeric data, will be inefficient. An index structure is the backbone of any database management system. It has been seen that index structures present in existing relational database management frameworks cannot handle multimedia data effectively. Thus, in this dissertation, a generalized multidimensional index structure is proposed which accommodates the atypical multidimensional representation and the semantic information carried by different multimedia data seamlessly from within one single framework. Additionally, the dissertation investigates the evolving relationships among multimedia data in a collaborative environment and how such information can help to customize the design of the proposed index structure, when it is used to manage multimedia data in a shared environment. Extensive experiments were conducted to present the usability and better performance of the proposed framework over current state-of-art approaches.

Essays of strategic alliance portfolio configuration—Its performance properties, strategic antecedents and consequential effects on multinational firms' continuing foreign expansion

This dissertation focused on an increasingly prevalent phenomenon in today's global business environment—strategic alliance portfolio. Building on resource-based view, resource dependency theory and real options theory, this dissertation adopted a multi-dimensional perspective to examine the performance implications, strategic antecedents of alliance portfolio configuration, and its strategic effects on firms' decision-making on their continuing foreign expansion. The dissertation consisted of three interrelated essays, each of which dealt with a specific research question. In the first essay I applied a two-dimensional construct that embraces both alliance relations' and alliance partners' attributes to illustrate alliance portfolio configuration. Based on this framework, a longitudinal study was conducted attempting to explore the performance properties of alliance portfolio configuration. The results revealed that alliance diversity and partner diversity have different relative contributions to firms' economic performance. The relationship between alliance portfolio configuration and firm performance was shaped by degree of multinationality in a curvilinear pattern. The second essay attempted to identify the firm level driving forces of alliance portfolio configuration and how these forces interacting with firms' internationalization influence firms' strategic choices on alliance portfolio configuration. The empirical results indicated that past alliance experience, slack resource and firms' brand images are three critical determinants shaping alliance portfolios, but those shaping relationships are conditioned by firms' multinationality. The third essay primarily employed real options theory to build a conceptual framework, revealing how country-, alliance portfolio-, firm-, and industry level factors and their interactions influence firms' strategic decision-making on post-entry continuing expansion in foreign markets. The two empirical studies were resided in global hospitality and travel industries and use panel data to test the relevant theoretical models. Overall, the dissertation advanced and enriched the theoretical domain of alliance portfolio. It particularly shed valuable insights on three fundamental questions in the domain of alliance portfolio research, namely "if and how alliance portfolios contribute to firms' economic performance"; "what determines the appearance of alliance portfolios”; and "how alliance portfolios affect firms' strategic decision-making". This dissertation also extended the international business and strategic management research on service multinationals' foreign expansion and performance.

Nonnative African jewelfish are more fit but not bolder at the invasion front: a trait comparison across an Everglades range expansion

This material is based upon work supported by the National Science Foundation through the Florida Coastal Everglades Long-Term Ecological Research program under Cooperative Agreements #DBI-0620409 and #DEB-9910514. Any opinions, findings, conclusions, or recommendations expressed in the material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.