Solution mining for specific contextualised problems (original) (raw)
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Analyzing Problem Frames together with Solution Patterns
inf.puc-rio.br, 2007
The Problem Frames approach defines identifiable problem classes based on, among other things, their context and the characteristics of their domains, interfaces and requirements, without going deeply into the solution. Other software engineering approaches deal with the ...
A context model for knowledge-intensive case-based reasoning
1998
Decision-support systems that help solving problems in open and weak theory domains, i.e. hard problems, need improved methods to ground their models in real world situations. Models that attempt to capture domain knowledge in terms of, e.g. rules or deeper relational networks, tend either to become too abstract to be efficient, or too brittle to handle new problems. In our research we study how the incorporation of case-specific, episodic, knowledge enables such systems to become more robust and to adapt to a changing environment by continuously retaining new problem solving cases as they occur during normal system operation. The research reported in this paper describes an extension that incorporates additional knowledge of the problem solving context into the architecture. The components of this context model is described, and related to the roles the components play in an abductive diagnostic process. Background studies are summarized, the context model is explained, and an example shows its integration into an existing knowledge-intensive CBR system.
Acquisition of generic problem solving knowledge through information extraction and pattern mining
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Case-based problem solving for knowledge management systems
1999
Case Based Reasoning (CBR) is an intelligent systems methodology that enables information managers to increase efficiency and reduce cost by substantially automating processes (i.e., diagnosis, scheduling, or design). By identifying and ranking the relevance between a new case and previously encountered cases (i.e., stored in the case base), CBR systems can capture and share all of an organizationÕs related knowledge capital for future use, and knowledge recycling can optimize resources spent o n research and development. Unfamiliar cases are solved and documented by retrieving and adapting solutions from similar stored cases. Sample applications include a proposed knowledge system designed to enhance the NASA-KSC Shuttle Processing Out-of-Family Disposition process, which addresses any operation or performance outside expected range or one that has not previously been experienced. CBR technology can yield productive results b y transforming problem report and interim problem report related documentation into explicit knowledge that can be reused to obtain solutions for new anomalies. Applying CBR technology to the Out-of-Family Disposition process can transform the organization into a learning organization that continues to grow in intellectual capital and related applied knowledge. This paper discusses the application of the NaCoDAE Conversational CBR (CCBR) system for this process. NaCoDAE is a software package developed at the Naval Research Laboratory. It uses CCBR technology t o store cases, questions, and actions; and has a built-in method that efficiently searches for the most relevant cases. practices", forecasts, and fixes. Technologically, Intranets, GroupWare, data warehouses, networks, bulletin boards, and video-conferencing are key tools for storing and distributing this intelligence (Maglitta, 1996). Case-Based Reasoning Case-based reasoning (CBR) is a methodology where past experiences with corresponding solutions are stored in an
Towards design principles for effective context-and perspective-based web mining
Proceedings of the 4th …, 2009
A practical and scalable web mining solution is needed that can assist the user in processing existing web-based resources to discover specific, relevant information content. This is especially important for researcher communities where data deployed on the World Wide Web are characterized by autonomous, dynamically evolving, and conceptually diverse information sources. The paper describes a systematic design research study that is based on prototyping/evaluation and abstraction using existing and new techniques incorporated as plug and play components into a research workbench. The study investigates an approach, DISCOVERY, for using (1) context/perspective information and (2) social networks such as ODP or Wikipedia for designing practical and scalable human-web systems for finding web pages that are relevant and meet the needs and requirements of a user or a group of users. The paper also describes the current implementation of DISCOVERY and its initial use in finding web pages in a targeted web domain. The resulting system arguably meets the common needs and requirements of a group of people based on the information provided by the group in the form of a set of context web pages. The system is evaluated for a scenario in which assistance of the system is sought for a group of faculty members in finding NSF research grant opportunities that they should collaboratively respond to, utilizing the context provided by their recent publications.
A Case-based Reasoning System to Support the Global Software Development
Procedia Computer Science, 2014
In addition to the benefits brought by the use of Distributed Software Development, new challenges linked to its use also emerged. Due to lack of information companies and organizations around the globe, independently, solve these challenges in many different ways, each with their practices, some more some less efficient, where best practices are hardly widespread among DDS community. In this context, this paper aims to present a web system based on Case Based Reasoning and Natural Language Processing to extract information in text form of problems and solutions adopted by distributed software projects and to recommend similar past experiences in order to support the decisions and resolutions of problems arising from new situations in distributed projects. The feasibility proof of the method was made from experimental tests conducted to identify the success of the recommendations of previous valid cases, with a success rate of 90% for the sample used.
On Some Lessons from Modeling Contexts in Complex Problem Solving in Information Technology
Journal of Information Technology Research, 2000
This paper reviews current research on context in problem solving and existing two-dimensional frameworks for expressing project contexts in Systems Thinking and Software Engineering. It makes the case for modeling of context with Problem Structuring Methods. The authors present lessons learnt from applying such methods in the context of their experience with several complex management interventions in Information and Telecommunications Technologies. The paper aims to contribute to the understanding of project contexts in complex problem solving in Information Technology.
Algorithmically Exploiting the Knowledge Accumulated in Textual Domains for Technical Support
2021
Processing natural language and extract relevant information in deep technical engineering domain remains an open challenge. On the other side, manufacturers of high-value assets which often deliver product services through the equipment life, supporting maintenance, spare parts management and remote monitoring and diagnostics for issues resolution, have availability of a good amount of textual data containing technical cases with a certain engineering depth. This paper presents a case study in which various Artificial Intelligence algorithms were applied to historical technical cases to extract know-how useful to help technicians in approaching new cases. Initially the work process and available data are presented; the focus is on the outbound communication delivered from the technical team to the site operators, that is structured in 3 main paragraphs: event description, technical assessment, recommended actions. The work proceeded in two parallel streams: the first concerned the ...
Applications of case‐based reasoning in Software Engineering: a systematic mapping study
IET Software, 2014
Domain knowledge for various decision-making activities of Software Engineering (SE) is rarely available in a structured or well-formalised form. Owing to lack of the well-informed knowledge, decision making for different kinds of predictions and estimations in SE domain is a challenge. Maintenance and elicitation of domain knowledge is an overwhelming task and causes the knowledge acquisition bottleneck. Most of the artificial intelligence techniques of prediction and estimation do not work in absence of complete and structured knowledge. Case-based reasoning (CBR) is a lazy learning paradigm of artificial intelligence which takes care of this challenge and helps to reduce the knowledge availability bottleneck. This technique exploits the similar experience of past which may be available in unstructured form, and improves its learning curve with passage of time. In literature, CBR has been successfully applied in various areas of SE, but there is lack of single systematic panoramic picture which might have highlighted the potential research questions in this direction. In this study, the author has presented a comprehensive and panoramic systematic mapping study of various CBR applications in SE domain, and identified some promising future research directions. 2 Case-based reasoning The CBR is a lazy machine learning approach. It relies on an archive storing 'cases', that is, previously solved problems and their respective solutions. This archive is known as 'case-base'. Aided by this knowledge repository, the CBR model solves new unseen problems by exploiting its learnt experience. The CBR engine goes through a series of steps
International Journal of Electrical and Computer Engineering (IJECE), 2022
Online communities are a real medium for human experiences sharing. They contain rich knowledge of lived situations and experiences that can be used to support decision-making process and problem-solving. This work presents an approach for extracting, representing, and evaluating components of problem-solving knowledge shared in online communities. Few studies have tackled the issue of knowledge extraction and its usefulness evaluation in online communities. In this study, we propose a new approach to detect and evaluate best solutions to problems discussed by members of online communities. Our approach is based on knowledge graph technology and graphs theory enabling the representation of knowledge shared by the community and facilitating its reuse. Our process of problem-solving knowledge extraction in online communities (PSKEOC) consists of three phases: problems and solutions detection and classification, knowledge graph constitution and finally best solutions evaluation. The experimental results are compared to the World Health Organization (WHO) model chapter about Infant and young child feeding and show that our approach succeed to extract and reveal important problem-solving knowledge contained in online community’s conversations. Our proposed approach leads to the construction of an experiential knowledge graph as a representation of the constructed knowledge base in the community studied in this paper.