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A Framework for Discovery and Diagnosis of Behavioral Transitions in Event-streamsAkhlaghi, Arash 18 December 2013 (has links)
Date stream mining techniques can be used in tracking user behaviors as they attempt to achieve their goals. Quality metrics over stream-mined models identify potential changes in user goal attainment. When the quality of some data mined models varies significantly from nearby models—as defined by quality metrics—then the user’s behavior is automatically flagged as a potentially significant behavioral change. Decision tree, sequence pattern and Hidden Markov modeling being used in this study. These three types of modeling can expose different aspect of user’s behavior. In case of decision tree modeling, the specific changes in user behavior can automatically characterized by differencing the data-mined decision-tree models. The sequence pattern modeling can shed light on how the user changes his sequence of actions and Hidden Markov modeling can identifies the learning transition points. This research describes how model-quality monitoring and these three types of modeling as a generic framework can aid recognition and diagnoses of behavioral changes in a case study of cognitive rehabilitation via emailing. The date stream mining techniques mentioned are used to monitor patient goals as part of a clinical plan to aid cognitive rehabilitation. In this context, real time data mining aids clinicians in tracking user behaviors as they attempt to achieve their goals. This generic framework can be widely applicable to other real-time data-intensive analysis problems. In order to illustrate this fact, the similar Hidden Markov modeling is being used for analyzing the transactional behavior of a telecommunication company for fraud detection. Fraud similarly can be considered as a potentially significant transaction behavioral change.
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A situation refinement model for complex event processingAlakari, Alaa A. 07 January 2021 (has links)
Complex Event Processing (CEP) systems aim at processing large flows of events
to discover situations of interest (SOI). Primarily, CEP uses predefined pattern templates
to detect occurrences of complex events in an event stream. Extracting complex
event is achieved by employing techniques such as filtering and aggregation to detect
complex patterns of many simple events. In general, CEP systems rely on domain
experts to de fine complex pattern rules to recognize SOI. However, the task of fine
tuning complex pattern rules in the event streaming environment face two main challenges:
the issue of increased pattern complexity and the event streaming constraints
where such rules must be acquired and processed in near real-time.
Therefore, to fine-tune the CEP pattern to identify SOI, the following requirements
must be met: First, a minimum number of rules must be used to re fine the CEP pattern
to avoid increased pattern complexity, and second, domain knowledge must be
incorporated in the refinement process to improve awareness about emerging situations.
Furthermore, the event data must be processed upon arrival to cope with
the continuous arrival of events in the stream and to respond in near real-time.
In this dissertation, we present a Situation Refi nement Model (SRM) that considers
these requirements. In particular, by developing a Single-Scan Frequent Item
Mining algorithm to acquire the minimal number of CEP rules with the ability to
adjust the level of re refinement to t the applied scenario. In addition, a cost-gain
evaluation measure to determine the best tradeoff to identify a particular SOI is
presented. / Graduate
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