Achieving self-healing in service delivery software systems by means of case-based reasoning

Self-healing, i.e. the capability of a system to autonomously detect failures and recover from them, is a very attractive property that may enable large-scale software systems, aimed at delivering services on a 24/7 fashion, to meet their goals with little or no human intervention. Achieving self-healing requires the elicitation and maintenance of domain knowledge in the form of 〈service failure diagnosis, repair plan〉 patterns, a task which can be overwhelming. Case-Based Reasoning (CBR) is a lazy learning paradigm that largely reduces this kind of knowledge acquisition bottleneck. Moreover, the application of CBR for failure diagnosis and remediation in software systems appears to be very suitable, as in this domain most errors are re-occurrences of known problems. In this paper, we describe a CBR approach for providing large-scale, distributed software systems with self-healing capabilities, and demonstrate the practical applicability of our methodology by means of some experimental results on a real world application.     

Decision-theoretic case-based reasoning

We describe a decision-theoretic methodology for case-based reasoning in diagnosis and troubleshooting applications. The system utilizes a special-structure Bayesian network to represent diagnostic cases, with nodes representing issues, causes, and symptoms. Dirichlet distributions are assessed at knowledge acquisition time to indicate the strength of relationships between variables. During a diagnosis session, a relevant subnetwork is extracted from a Bayesian-network database that describes a very large number of diagnostic interactions and cases. The constructed network is used to make recommendations regarding possible repairs and additional observations, based on an estimate of expected repair costs. As cases are resolved, observations of issues, causes, symptoms, and the success of repairs are recorded. New variables are added to the database, and the probabilities associated with variables already in the database are updated. In this way, the inferential behavior of system adjusts to the characteristics of the target population of users. We show how these elements work together in a cycle of troubleshooting tasks, and describe some results from a pilot system implementation and deployment     

Financial market monitoring by case-based reasoning

This paper shows that case-based reasoning (CBR), an artificial intelligence technique, is a quite efficient tool in monitoring financial market against its possible collapse. For this purpose, daily financial condition indicator (DFCI) monitoring financial market is built on CBR and its performance is compared to DFCI on neural network. This study is empirically done for the Korean financial market.     

Continuous case-based reasoning

Case-based reasoning systems have traditionally been used to perform high-level reasoning in problem domains that can be adequately described using discrete, symbolic representations. However, many real-world problem domains, such as autonomous robotic navigation, are better characterized using continuous representations. Such problem domains also require continuous performance, such as on-line sensorimotor interaction with the environment, and continuous adaptation and learning during the performance task. This article introduces a new method for continuous case-based reasoning, and discusses its application to the dynamic selection, modification, and acquisition of robot behaviors in an autonomous navigation system, SINS (self-improving navigation system). The computer program and the underlying method are systematically evaluated through statistical analysis of results from several empirical studies. The article concludes with a general discussion of case-based reasoning issues addressed by this research.     

From case-based reasoning to traces-based reasoning

CBR is an original AI paradigm based on the adaptation of solutions of past problems in order to solve new similar problems. Hence, a case is a problem with its solution and cases are stored in a case library. The reasoning process follows a cycle that facilitates “learning” from new solved cases. This approach can be also viewed as a lazy learning method when applied for task classification. CBR is applied for various tasks as design, planning, diagnosis, information retrieval, etc. The paper is the occasion to go a step further in reusing past unstructured experience, by considering traces of computer use as experience knowledge containers for situation based problem solving.     

A schema aware ETL workflow generator

Extract, Transform and Load (ETL) processes organized as workflows play an important role in data warehousing. As ETL workflows are usually complex, various ETL facilities have been developed to address their control-flow process modeling and execution control. To evaluate the quality of ETL facilities, Synthetic ETL workflow test cases, consisting of control-flow and data-flow aspects are needed to check ETL facility functionalities at construction time and to validate the correctness and performance of ETL facilities at run time. Although there are some synthetic workflow and data set test case generation approaches existed in literatures, little work is done to consider both aspects at the same time specifically for ETL workflow generators. To address this issue, this paper proposes a schema aware ETL workflow generator with which users can characterize their ETL workflows by various parameters and get ETL workflow test cases with control-flow of ETL activities, complied schemas and associated recordsets. Our generator consists of three steps. First, with type and ratio of individual activities and their connection characteristic parameter specification, the generator will produce ETL activities and form ETL skeleton which determine how generated activities are cooperated with each other. Second, with schema transformation characteristic parameter specification, e.g. ranges of numbers of attributes, the generator will resolve attribute dependencies and refine input/output schemas with complied attributes and their data types. In the last step, recordsets are generated following cardinality specifications. ETL workflows in specific patterns are produced in the experiment in order to show the ability of our generator. Also experiments to generate thousands of ETL workflow test cases in seconds have been done to verify the usability of the generator.     

An introduction to case-based reasoning

Case-based reasoning means using old experiences to understand and solve new problems. In case-based reasoning, a reasoner remembers a previous situation similar to the current one and uses that to solve the new problem. Case-based reasoning can mean adapting old solutions to meet new demands; using old cases to explain new situations; using old cases to critique new solutions; or reasoning from precedents to interpret a new situation (much like lawyers do) or create an equitable solution to a new problem (much like labor mediators do). This paper discusses the processes involved in case-based reasoning and the tasks for which case-based reasoning is useful.This article is excerpted from Case-Based Reasoning by Janet Kolodner, to be published by Morgan-Kaufmann Publishers, Inc. in 1992.This work was partially funded by darpa under Contract No. F49620-88-C-0058 monitored by AFOSR, by NSF under Grant No. IST-8608362, and by ARI under Contract No. MDA-903-86-C-173.     

混合案例推理和规则推理的集成推理框架

在智能决策系统（IDSS）获取知识的推理体系中，案例推理和规则推理有着各自的优点，而混合两者的集成推理可以克服两者的缺点，提高系统的效率和综合推理能力。但是集成推理系统缺乏通用性，延长了开发周期，且不利于规则库和案例库的重用。一种可扩充的集成推理框架为了解决上面的问题而被提出，该框架利用智能决策支持语言Knonit的组件性，对不同的集成方式可方便地扩充相应的集成推理方案，从而快速地搭建IDSS应用；同时规则和案例是作为Knonit广义知识元存在，可以在集成推理框架中复用，另一方面，Knonit的动态特性和可扩充性也对案例库和知识库动态的调整和扩充提供了支持。     

Zero anaphora resolution by case-based reasoning and pattern conceptualization

Effective anaphora resolution is helpful to many applications of natural language processing such as machine translation, summarization and question answering. In this paper, a novel resolution approach is proposed to tackle zero anaphora, which is the most frequent type of anaphora shown in Chinese texts. Unlike most of the previous approaches relying on hand-coded rules, our resolution is mainly constructed by employing case-based reasoning and pattern conceptualization. Moreover, the resolution is incorporated with the mechanisms to identify cataphora and non-antecedent instances so as to enhance the resolution performance. Compared to a general rule-based approach, the proposed approach indeed improves the resolution performance by achieves 78% recall and 79% precision on solving 1051 zero anaphora instances in 382 narrative texts.     

Synergistic case-based reasoning in medical domains

This paper presents four synergistic systems that exemplify the approaches and benefits of case-based reasoning in medical domains. It then explores how these systems couple Artificial Intelligence (AI) research with medical research and practice, integrate multiple AI and computing methodologies, leverage small numbers of available cases, reason with time series data, and integrate numeric data with contextual and subjective information. The following systems are presented: (1) CARE-PARTNER, which supports the long-term follow-up care of stem-cell transplantation patients; (2) the 4 Diabetes Support System, which aids in managing patients with type 1 diabetes on insulin pump therapy; (3) Retrieval of HEmodialysis in NEphrological Disorders, which supports hemodialysis treatment of patients with end stage renal disease; and (4) the Mälardalen Stress System, which aids in the diagnosis and treatment of stress-related disorders.     

Robotic inspection plan optimizationby case-based reasoning

Inspection planning is discussed in a framework where a rich choice of instruments is available and robots can also participate in the inspection process. The problem of constrained plan optimization is exposed, and a solution is suggested that is based on task grouping. After outlining the overall planning process, we give details of the optimization stage where case-based reasoning is applied. Finally, it will be shown how the implemented knowledge-based system can operate as a knowledge server.     

Scale-guided object matching for case-based reasoning

Case-Based Reasoning (CBR) can be seen as a problem-solving paradigm that advocates the use of previous experiences to limit search spaces and to reduce opportunities for error repetition. In this paradigm, the case at hand is compared against former experiences to select from a set of possible courses of action the best one. A comparison method is required to ensure that the most resembling experience is, in fact, chosen to drive the problem-solving process. This paper discusses an object-oriented framework that provides a scale-guided measure of similarity between objects, and shows how this framework can be applied for case-based reasoning, drawing examples from device diagnosis.     

基于实例推理系统中的权重分析

指标权重的确定在基于实例推理(CBR)系统的检索模型中起着重要的作用。采用基于多位专家的二级模糊综合评判方法求得各个指标的总的综合权重,对指标权重进行了讨论,并引入关联度的概念,讨论了各专家的偏离度及一致性。实例证明有效、可行。     

Hierarchical case-based reasoning integrating case-based anddecompositional problem-solving techniques for plant-control softwaredesign

Case based reasoning (CBR) is an artificial intelligence technique that emphasises the role of past experience during future problem solving. New problems are solved by retrieving and adapting the solutions to similar problems, solutions that have been stored and indexed for future reuse as cases in a case-base. The power of CBR is severely curtailed if problem solving is limited to the retrieval and adaptation of a single case, so most CBR systems dealing with complex problem solving tasks have to use multiple cases. The paper describes and evaluates the technique of hierarchical case based reasoning, which allows complex problems to be solved by reusing multiple cases at various levels of abstraction. The technique is described in the context of Deja Vu, a CBR system aimed at automating plant-control software design     

Early diagnosis of gastrointestinal cancer by using case-based and rule-based reasoning

In this paper, we present a medical diagnosis decision support model for gastrointestinal cancer. It should be used by general practitioners whenever there is a suspicion that a patient has this type of cancer. To build our model, we used Case-Based Reasoning (CBR) and Rule-Based Reasoning (RBR). We used real patient data as inputs to our model. We applied RBR to improve the CBR retrieve process. The model’s output presents the probability of the patient having a specific cancer. In order to adjust the attributes weights, we collected data from a general practitioner. To validate our model, we used K-fold cross validation and the paired t-test. The results showed that, with our approach, the accuracy of the diagnosis increased by 22.92% when compared to a CBR approach not using RBR in case retrieval. Furthermore, we evaluated our approach with an online questionnaire and semi-structured interviews. Even though, given the number of respondents, we cannot generalize our conclusions, the results indicate that our approach would be useful for general practitioners.     

Mining competent case bases for case-based reasoning

Case-based reasoning relies heavily on the availability of a highly competent case base to make high-quality decisions. However, good case bases are difficult to come by. In this paper, we present a novel algorithm for automatically mining a high-quality case base from a raw case set that can preserve and sometimes even improve the competence of case-based reasoning. In this paper, we analyze two major problems in previous case-mining algorithms. The first problem is caused by noisy cases such that the nearest neighbor cases of a problem may not provide correct solutions. The second problem is caused by uneven case distribution, such that similar problems may have dissimilar solutions. To solve these problems, we develop a theoretical framework for the error bound in case-based reasoning, and propose a novel case-base mining algorithm guided by the theoretical results that returns a high-quality case base from raw data efficiently. We support our theory and algorithm with extensive empirical evaluation using different benchmark data sets.     

Diabetic patients management exploiting case-based reasoning techniques

In this paper we propose a case-based decision support tool, designed to help physicians in 1st type diabetes therapy revision through the intelligent retrieval of data related to past situations (or 'cases') similar to the current one. A case is defined as a set of variable values (or features) collected during a visit. We defined taxonomy of prototypical patients' conditions, or classes, to which each case should belong. For each input case, the system allows the physician to find similar past cases, both from the same patient and from different ones. We have implemented a two-steps procedure; (1) it finds the classes to which the input case could belong; (2) it lists the most similar cases from these classes, through a nearest neighbor technique, and provides some statistics useful for decision taking. The performance of the system has been tested on a data-base of 147 real cases, collected at the Policlinico S. Matteo Hospital of Pavia. The tool is fully integrated in the web-based architecture of the EU funded Telematic management of Insulin Dependent Diabetes Mellitus (T-IDDM) project.     

An exercise in formalising teleological case-based reasoning

This paper takes up Berman and Hafner's (1993) challenge to model legal case-based reasoning not just in terms of factual similarities and differences but also in terms of the values that are at stake. The formal framework of Prakken and Sartor (1998) is applied to examples of case-based reasoning involving values, and a method for formalising such examples is proposed. The method makes it possible to express that a case should be decided in a certain way because that advances certain values. The method also supports the comparison of conflicting precedents in terms of values, and it supports debates on the relevance of distinctions in terms of values.