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Modern forms of computing systems are becoming progressively more complex, with an increasing number of heterogeneous hardware and software components. As a result, it is quite challenging to manage these complex systems and meet the requirements in manageability, dependability, and performance that are demanded by enterprise customers. This survey presents a variety of data-driven techniques and applications with a focus on computing system management. In particular, the survey introduces intelligent methods for event generation that can transform diverse log data sources into structured events, reviews different types of event patterns and the corresponding event-mining techniques, and summarizes various event summarization methods and data-driven approaches for problem diagnosis in system management. We hope this survey will provide a good overview for data-driven techniques in computing system management.

In system management applications, an overwhelming amount of data are generated and collected in the form of temporal events. While mining temporal event data to discover interesting and frequent patterns has obtained rapidly increasing research efforts, users of the applications are overwhelmed by the mining results. The extracted patterns are generally of large volume and hard to interpret, they may be of no emphasis, intricate and meaningless to non-experts, even to domain experts. While traditional research efforts focus on finding interesting patterns, in this paper, we take a novel approach called event summarization towards the understanding of the seemingly chaotic temporal data. Event summarization aims at providing a concise interpretation of the seemingly chaotic data, so that domain experts may take actions upon the summarized models. Event summarization decomposes the temporal information into many independent subsets and finds well fitted models to describe each subset.

In this poster, we propose a novel document summarization approach named Ontology-enriched M ulti-Document S ummarization(OMS ) for utilizing background knowledge to improve summarization results. OMS first maps the sentences of input documents onto an ontology, then links the given query to a specific node in the ontology, and finally extracts the summary from the sentences in the subtree rooted at the query node. By using the domain-related ontology, OMS can better capture the semantic relevance between the query and the sentences, and thus lead to better summarization results. As a byproduct, the final summary generated by OMS can be represented as a tree showing the hierarchical relationships of the extracted sentences. Evaluation results on the collection of press releases by Miami-Dade County Department of Emergency Management during Hurricane Wilma in 2005 demonstrate the efficacy of OMS.

Human errors, e.g. surgeon's misoperation, have been recognised as a critical cause to the large amount of medical accidents in hospital. Recently, the concept of Medical Cyber-Physical Systems (MCPS) has been proposed to enable automatic medical device coordination for patient protection. However, MCPS have limited capabilities to detect human errors because of only integrating medical devices, and thus, often result in late device coordination when patients are found to have already developed significant adverse physiological reactions. In this paper, we propose to build context-aware MCPS to avoid such risky situations. We leverage various nonmedical devices to capture implicit contextual information when human users are interacting with MCPS. By using these contexts, we significantly raise the system's awareness to human errors, and thus, allow it to take proper actions as early as possible to avoid the potential accidents. A major challenge in designing such systems, however, is how to deal with context uncertainty without sacrificing patient safety. Contexts are uncertain in nature, but false context detection can trigger unnecessary actions harmful to patient. To address this issue, we develop a novel scheme called 'context-assessment-action', where medical knowledge is utilised to assess all context-triggered actions and prohibit the risky ones. To our knowledge, our approach is the first to enable context-awareness for safety-critical systems. Finally, we apply this approach and conduct a case study on patient-controlled analgesia. Experimental results demonstrate the effectiveness of our approach and the great promise of context-aware MCPS for patient safety improvement.

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