Data Mining for Software Development Life Cycle Quality Management

Computer software plays an important role in business, government, society and sciences. To solve real-world problems, it is very important to measure the quality and reliability in the software development life cycle (SDLC). Software Engineering (SE) is the computing field concerned with designing, developing, implementing, maintaining and modifying software. The present paper gives an overview of the Data Mining (DM) techniques that can be applied to various types of SE data in order to solve the challenges posed by SE tasks such as programming, bug detection, debugging and maintenance. A specific DM software is discussed, namely one of the analytical tools for analyzing data and summarizing the relationships that have been identified. The paper concludes that the proposed techniques of DM within the domain of SE could be well applied in fields such as Customer Relationship Management (CRM), eCommerce and eGovernment. ACM Computing Classification System (1998): H.2.8.

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.

Salt springs from Romania exploited during early archaeological times : a new candidate for World Heritage

Our approach emphasizes on the importance of the first forms of salt springs exploitation meant to obtain recrystallized salt for the development of prehistoric human communities within the continental inlands of Europe. Although it does not compare with the monumental dimension of World Heritage, the exploitation of some salt springs in Eastern Romania goes back around 8 millennia; they may be the oldest such exploitations in the world, as proven by 14C calibrated data. What differentiates Romanian salt springs from other famous similar areas in Europe is the continuity of exploitation and utilization of natural brine. Actually, these resilient behaviours explain the creation of a whole and complex universe of salt, which also represents a unique point of reference within the intangible World Heritage. It is through this association in variable proportions between tangible (non-monumental) and intangible that these salt springs comprising the oldest traces of salt exploitation can be considered elements of World Heritage. Today, important personalities in the fields of archaeology, anthropology and history posit that salt is a major reference for the development of the entire umanity. Obviously, the breakthrough of this idea requires awareness efforts targeting, on one hand, local communities in those areas wand, on the other, national and international scientific and cultural environments concerned with the World Heritage. In this context, a proper motivation is the fact that the last two decades have witnessed an intensification of research on salt, which turned this topic one of the major themes within European archaeology and ethno-archaeology. In terms of local community awareness concerning the importance of salt springs in the economic development of a (micro) area over time, it is worth underlining mostly the specialists’ efforts of presenting this topic in the media. Moreover, the impact of a recent initiative of the two museums in the area (Piatra Neamț and Târgu Neamț)—establishing distinct sections that represent, by using museum-inspired means, both archaeological vestiges and traditional practices of natural brine exploitation and utilization—will prove its extent in time. Certain local authorities and private entrepreneurs have pinpointed that valorising tourist areas comprising the oldest traces of salt exploitation in Romania is an imminent issue. The greatest challenge is finding a balance between the civilization improvements (upgraded access roads, upgrading operating areas, etc.) and thep rotection of still-alive traditional practices of salt exploitation and use, within rural areas. Certain local authorities and private entrepreneurs have pinpointed that valorising tourist areas comprising the oldest traces of salt exploitation in Romania will become, sooner or later, an imminent issue. The greatest challenge is finding a balance between the civilization improvements (upgraded access roads, upgrading operating areas, etc.) and the protection of still-alive traditional practices of salt exploitation and use, within rural areas.

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.

Contextual text mining

With the dramatic growth of text information, there is an increasing need for powerful text mining systems that can automatically discover useful knowledge from text. Text is generally associated with all kinds of contextual information. Those contexts can be explicit, such as the time and the location where a blog article is written, and the author(s) of a biomedical publication, or implicit, such as the positive or negative sentiment that an author had when she wrote a product review; there may also be complex context such as the social network of the authors. Many applications require analysis of topic patterns over different contexts. For instance, analysis of search logs in the context of the user can reveal how we can improve the quality of a search engine by optimizing the search results according to particular users; analysis of customer reviews in the context of positive and negative sentiments can help the user summarize public opinions about a product; analysis of blogs or scientific publications in the context of a social network can facilitate discovery of more meaningful topical communities. Since context information significantly affects the choices of topics and language made by authors, in general, it is very important to incorporate it into analyzing and mining text data. In general, modeling the context in text, discovering contextual patterns of language units and topics from text, a general task which we refer to as Contextual Text Mining, has widespread applications in text mining. In this thesis, we provide a novel and systematic study of contextual text mining, which is a new paradigm of text mining treating context information as the ``first-class citizen.'' We formally define the problem of contextual text mining and its basic tasks, and propose a general framework for contextual text mining based on generative modeling of text. This conceptual framework provides general guidance on text mining problems with context information and can be instantiated into many real tasks, including the general problem of contextual topic analysis. We formally present a functional framework for contextual topic analysis, with a general contextual topic model and its various versions, which can effectively solve the text mining problems in a lot of real world applications. We further introduce general components of contextual topic analysis, by adding priors to contextual topic models to incorporate prior knowledge, regularizing contextual topic models with dependency structure of context, and postprocessing contextual patterns to extract refined patterns. The refinements on the general contextual topic model naturally lead to a variety of probabilistic models which incorporate different types of context and various assumptions and constraints. These special versions of the contextual topic model are proved effective in a variety of real applications involving topics and explicit contexts, implicit contexts, and complex contexts. We then introduce a postprocessing procedure for contextual patterns, by generating meaningful labels for multinomial context models. This method provides a general way to interpret text mining results for real users. By applying contextual text mining in the ``context'' of other text information management tasks, including ad hoc text retrieval and web search, we further prove the effectiveness of contextual text mining techniques in a quantitative way with large scale datasets. The framework of contextual text mining not only unifies many explorations of text analysis with context information, but also opens up many new possibilities for future research directions in text mining.

Reducing the global burden of depression : population level analysis of intervention cost-effectiveness in 14 world regions

International evidence on the cost and effects of interventions for reducing the global burden of depression remain scarce. Aims: To estimate the population-level cost-effectiveness of evidence-based depression interventions and their contribution towards reducing current burden. Method: Primary-care-based depression interventions were modelled at the level of whole populations in 14 epidemiological subregions of the world. Total population-level costs (in international dollars or I$) and effectiveness (disability adjusted life years (DALYs) averted) were combined to form average and incremental cost-effectiveness ratios. Results: Evaluated interventions have the potential to reduce the current burden of depression by 10–30%. Pharmacotherapy with older antidepressant drugs, with or without proactive collaborative care, are currently more cost-effective strategies than those using newer antidepressants, particularly in lower-income subregions. Conclusions: Even in resource-poor regions, each DALYaverted by efficient depression treatments in primary care costs less than 1 year of average per capita income, making such interventions a cost-effective use of health resources. However, current levels of burden can only be reduced significantlyif there is a substantialincrease substantial increase intreatment coverage.

Skill development for innovative procurement environments in construction

The construction industry should be a priority to all governments because it impacts economically and socially on all citizens. Sector turnover in industrialised economies typically averages 8-12% of GDP. Further, construction is critical to economic growth. Recent Australian studies estimate that a 10% gain in efficiency in construction translates to a 2.5% increase in GDP Inefficiencies in the Australian construction industry have been identified by a number of recent studies modelling the building process. They have identified potential savings in time of between 25% and 40% by reducing non-value added steps in the process. A culture of reform is now emerging in the industry – one in which alternate forms of project delivery are being trialed. Government and industry have identified Alliance Contracting as a means to increase efficiency in the construction industry as part of a new innovative procurement environment. Alliance contracting requires parties to form relationships and work cooperatively to provide a more complete service. This is a significant cultural change for the construction industry, with its well-known adversarial record in traditional contracting. Alliance contracts offer enormous potential benefits, but the Australian construction industry needs to develop new skills to effectively participate in the new relationship environment. This paper describes a collaborative project identifying skill needs for clients and construction professionals to more effectively participate in an increasingly sophisticated international procurement environment. The aim of identifying these skill needs is to assist industry, government, and skill developers to prepare the Australian construction workforce for the future. The collaborating Australian team has been fortunate to secure the Australian National Museum in Canberra as its live case study. The Acton Peninsula Development is the first major building development in the world awarded on the basis of a joint alliance contract.