Mining High Utility Patterns Over Data Streams

Mining useful patterns from sequential data is a challenging topic in data mining. An important task for mining sequential data is sequential pattern mining, which discovers sequences of itemsets that frequently appear in a sequence database. In sequential pattern mining, the selection of sequences is generally based on the frequency/support framework. However, most of the patterns returned by sequential pattern mining may not be informative enough to business people and are not particularly related to a business objective. In view of this, high utility sequential pattern (HUSP) mining has emerged as a novel research topic in data mining recently. The main objective of HUSP mining is to extract valuable and useful sequential patterns from data by considering the utility of a pattern that captures a business objective (e.g., profit, users interest). In HUSP mining, the goal is to find sequences whose utility in the database is no less than a user-specified minimum utility threshold. Nowadays, many applications generate a huge volume of data in the form of data streams. A number of studies have been conducted on mining HUSPs, but they are mainly intended for non-streaming data and thus do not take data stream characteristics into consideration. Mining HUSP from such data poses many challenges. First, it is infeasible to keep all streaming data in the memory due to the high volume of data accumulated over time. Second, mining algorithms need to process the arriving data in real time with one scan of data. Third, depending on the minimum utility threshold value, the number of patterns returned by a HUSP mining algorithm can be large and overwhelms the user. In general, it is hard for the user to determine the value for the threshold. Thus, algorithms that can find the most valuable patterns (i.e., top-k high utility patterns) are more desirable. Mining the most valuable patterns is interesting in both static data and data streams. To address these research limitations and challenges, this dissertation proposes techniques and algorithms for mining high utility sequential patterns over data streams. We work on mining HUSPs over both a long portion of a data stream and a short period of time. We also work on how to efficiently identify the most significant high utility patterns (namely, the top-k high utility patterns) over data streams. In the first part, we explore a fundamental problem that is how the limited memory space can be well utilized to produce high quality HUSPs over the entire data stream. An approximation algorithm, called MAHUSP, is designed which employs memory adaptive mechanisms to use a bounded portion of memory, to efficiently discover HUSPs over the entire data streams. The second part of the dissertation presents a new sliding window-based algorithm to discover recent high utility sequential patterns over data streams. A novel data structure named HUSP-Tree is proposed to maintain the essential information for mining recenT HUSPs. An efficient and single-pass algorithm named HUSP-Stream is proposed to generate recent HUSPs from HUSP-Tree. The third part addresses the problem of top-k high utility pattern mining over data streams. Two novel methods, named T-HUDS and T-HUSP, for finding top-k high utility patterns over a data stream are proposed. T-HUDS discovers top-k high utility itemsets and T-HUSP discovers top-k high utility sequential patterns over a data stream. T-HUDS is based on a compressed tree structure, called HUDS-Tree, that can be used to efficiently find potential top-k high utility itemsets over data streams. T-HUSP incrementally maintains the content of top-k HUSPs in a data stream in a summary data structure, named TKList, and discovers top-k HUSPs efficiently. All of the algorithms are evaluated using both synthetic and real datasets. The performances, including the running time, memory consumption, precision, recall and Fmeasure, are compared. In order to show the effectiveness and efficiency of the proposed methods in reallife applications, the fourth part of this dissertation presents applications of one of the proposed methods (i.e., MAHUSP) to extract meaningful patterns from a real web clickstream dataset and a real biosequence dataset. The utility-based sequential patterns are compared with the patterns in the frequency/support framework. The results show that high utility sequential pattern mining provides meaningful patterns in real-life applications

Mining Association Rules Events over Data Streams

Data streams have gained considerable attention in data analysis and data mining communities because of the emergence of a new classes of applications, such as monitoring, supply chain execution, sensor networks, oilfield and pipeline operations, financial marketing and health data industries. Telecommunication advancements have provided us with easy access to stream data produced by various applications. Data in streams differ from static data stored in data warehouses or database. Data streams are continuous, arrive at high-speeds and change through time. Traditional data mining algorithms assume presence of data in conventional storage means where data mining is performed centrally with the luxury of accessing the data multiple times, using powerful processors, providing offline output with no time constraints. Such algorithms are not suitable for dynamic data streams. Stream data needs to be mined promptly as it might not be feasible to store such volume of data. In addition, streams reflect live status of the environment generating it, so prompt analysis may provide early detection of faults, delays, performance measurements, trend analysis and other diagnostics. This thesis focuses on developing a data stream association rule mining algorithm among co-occurring events. The proposed algorithm mines association rules over data streams incrementally in a centralized setting. We are interested in association rules that meet a provided minimum confidence threshold and have a lift value greater than 1. We refer to such association rules as strong rules. Experiments on several datasets demonstrate that the proposed algorithms is efficient and effective in extracting association rules from data streams, thus having a faster processing time and better memory management

Collaborative Planning and Event Monitoring Over Supply Chain Network

The shifting paradigm of supply chain management is manifesting increasing reliance on automated collaborative planning and event monitoring through information-bounded interaction across organizations. An end-to-end support for the course of actions is turning vital in faster incident response and proactive decision making. Many current platforms exhibit limitations to handle supply chain planning and monitoring in decentralized setting where participants may divide their responsibilities and share computational load of the solution generation. In this thesis, we investigate modeling and solution generation techniques for shared commodity delivery planning and event monitoring problems in a collaborative setting. In particular, we first elaborate a new model of Multi-Depot Vehicle Routing Problem (MDVRP) to jointly serve customer demands using multiple vehicles followed by a heuristic technique to search near-optimal solutions for such problem instances. Secondly, we propose two distributed mechanisms, namely: Passive Learning and Active Negotiation, to find near-optimal MDVRP solutions while executing the heuristic algorithm at the participant's side. Thirdly, we illustrate a collaboration mechanism to cost-effectively deploy execution monitors over supply chain network in order to collect in-field plan execution data. Finally, we describe a distributed approach to collaboratively monitor associations among recent events from an incoming stream of plan execution data. Experimental results over known datasets demonstrate the efficiency of the approaches to handle medium and large problem instances. The work has also produced considerable knowledge on the collaborative transportation planning and execution event monitoring

Mining Time-Changing Data Streams

Streaming data have gained considerable attention in database and data mining communities because of the emergence of a class of applications, such as financial marketing, sensor networks, internet IP monitoring, and telecommunications that produce these data. Data streams have some unique characteristics that are not exhibited by traditional data: unbounded, fast-arriving, and time-changing. Traditional data mining techniques that make multiple passes over data or that ignore distribution changes are not applicable to dynamic data streams. Mining data streams has been an active research area to address requirements of the streaming applications. This thesis focuses on developing techniques for distribution change detection and mining time-changing data streams. Two techniques are proposed that can detect distribution changes in generic data streams. One approach for tackling one of the most popular stream mining tasks, frequent itemsets mining, is also presented in this thesis. All the proposed techniques are implemented and empirically studied. Experimental results show that the proposed techniques can achieve promising performance for detecting changes and mining dynamic data streams

Methods for frequent pattern mining in data streams within the MOA system

IncMine is a robust, efficient, practical, usable and extendable solution to perform Frequent Itemset mining over data streams. It is implementend under the Massive Online Analysis framework. It includes an analysis over its performances and its reaction to synthetic and real concept drift

Requirements and Use Cases ; Report I on the sub-project Smart Content Enrichment

In this technical report, we present the results of the first milestone phase of the Corporate Smart Content sub-project "Smart Content Enrichment". We present analyses of the state of the art in the fields concerning the three working packages defined in the sub-project, which are aspect-oriented ontology development, complex entity recognition, and semantic event pattern mining. We compare the research approaches related to our three research subjects and outline briefly our future work plan

High Performance Analytics in Complex Event Processing

Complex Event Processing (CEP) is the technical choice for high performance analytics in time-critical decision-making applications. Although current CEP systems support sequence pattern detection on continuous event streams, they do not support the computation of aggregated values over the matched sequences of a query pattern. Instead, aggregation is typically applied as a post processing step after CEP pattern detection, leading to an extremely inefficient solution for sequence aggregation. Meanwhile, the state-of-art aggregation techniques over traditional stream data are not directly applicable in the context of the sequence-semantics of CEP. In this paper, we propose an approach, called A-Seq, that successfully pushes the aggregation computation into the sequence pattern detection process. A-Seq succeeds to compute aggregation online by dynamically recording compact partial sequence aggregation without ever constructing the to-be-aggregated matched sequences. Techniques are devised to tackle all the key CEP- specific challenges for aggregation, including sliding window semantics, event purging, as well as sequence negation. For scalability, we further introduce the Chop-Connect methodology, that enables sequence aggregation sharing among queries with arbitrary substring relationships. Lastly, our cost-driven optimizer selects a shared execution plan for effectively processing a workload of CEP aggregation queries. Our experimental study using real data sets demonstrates over four orders of magnitude efficiency improvement for a wide range of tested scenarios of our proposed A-Seq approach compared to the state-of-art solutions, thus achieving high-performance CEP aggregation analytics