Online and Non-Parametric Drift Detection Methods Based on Hoeffding’s Bounds

I. Frías-Blanco, J. d. Campo-Ávila, G. Ramos-Jiménez, R. Morales-Bueno, A. Ortiz-Díaz and Y. Caballero-Mota, "Online and Non-Parametric Drift Detection Methods Based on Hoeffding’s Bounds," in IEEE Transactions on Knowledge and Data Engineering, vol. 27, no. 3, pp. 810-823, 1 March 2015 doi: 10.1109/TKDE.2014.2345382. © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Incremental and online learning algorithms are more relevant in the data mining context because of the increasing necessity to process data streams. In this context, the target function may change over time, an inherent problem of online learning (known as concept drift). In order to handle concept drift regardless of the learning model, we propose new methods to monitor the performance metrics measured during the learning process, to trigger drift signals when a significant variation has been detected. To monitor this performance, we apply some probability inequalities that assume only independent, univariate and bounded random variables to obtain theoretical guarantees for the detection of such distributional changes. Some common restrictions for the online change detection as well as relevant types of change (abrupt and gradual) are considered. Two main approaches are proposed, the first one involves moving averages and is more suitable to detect abrupt changes. The second one follows a widespread intuitive idea to deal with gradual changes using weighted moving averages. The simplicity of the proposed methods, together with the computational efficiency make them very advantageous. We use a Naïve Bayes classifier and a Perceptron to evaluate the performance of the methods over synthetic and real data.Supported in part by the SESAAME project number TIN2008-06582-C03-03 of the MICINN, Spain. Supported in part by the AUIP (Asociación Universitaria Iberoamericana de Postgrado)

Process mining : conformance and extension

Today’s business processes are realized by a complex sequence of tasks that are performed throughout an organization, often involving people from different departments and multiple IT systems. For example, an insurance company has a process to handle insurance claims for their clients, and a hospital has processes to diagnose and treat patients. Because there are many activities performed by different people throughout the organization, there is a lack of transparency about how exactly these processes are executed. However, understanding the process reality (the "as is" process) is the first necessary step to save cost, increase quality, or ensure compliance. The field of process mining aims to assist in creating process transparency by automatically analyzing processes based on existing IT data. Most processes are supported by IT systems nowadays. For example, Enterprise Resource Planning (ERP) systems such as SAP log all transaction information, and Customer Relationship Management (CRM) systems are used to keep track of all interactions with customers. Process mining techniques use these low-level log data (so-called event logs) to automatically generate process maps that visualize the process reality from different perspectives. For example, it is possible to automatically create process models that describe the causal dependencies between activities in the process. So far, process mining research has mostly focused on the discovery aspect (i.e., the extraction of models from event logs). This dissertation broadens the field of process mining to include the aspect of conformance and extension. Conformance aims at the detection of deviations from documented procedures by comparing the real process (as recorded in the event log) with an existing model that describes the assumed or intended process. Conformance is relevant for two reasons: 1. Most organizations document their processes in some form. For example, process models are created manually to understand and improve the process, comply with regulations, or for certification purposes. In the presence of existing models, it is often more important to point out the deviations from these existing models than to discover completely new models. Discrepancies emerge because business processes change, or because the models did not accurately reflect the real process in the first place (due to the manual and subjective creation of these models). If the existing models do not correspond to the actual processes, then they have little value. 2. Automatically discovered process models typically do not completely "fit" the event logs from which they were created. These discrepancies are due to noise and/or limitations of the used discovery techniques. Furthermore, in the context of complex and diverse process environments the discovered models often need to be simplified to obtain useful insights. Therefore, it is crucial to be able to check how much a discovered process model actually represents the real process. Conformance techniques can be used to quantify the representativeness of a mined model before drawing further conclusions. They thus constitute an important quality measurement to effectively use process discovery techniques in a practical setting. Once one is confident in the quality of an existing or discovered model, extension aims at the enrichment of these models by the integration of additional characteristics such as time, cost, or resource utilization. By extracting aditional information from an event log and projecting it onto an existing model, bottlenecks can be highlighted and correlations with other process perspectives can be identified. Such an integrated view on the process is needed to understand root causes for potential problems and actually make process improvements. Furthermore, extension techniques can be used to create integrated simulation models from event logs that resemble the real process more closely than manually created simulation models. In Part II of this thesis, we provide a comprehensive framework for the conformance checking of process models. First, we identify the evaluation dimensions fitness, decision/generalization, and structure as the relevant conformance dimensions.We develop several Petri-net based approaches to measure conformance in these dimensions and describe five case studies in which we successfully applied these conformance checking techniques to real and artificial examples. Furthermore, we provide a detailed literature review of related conformance measurement approaches (Chapter 4). Then, we study existing model evaluation approaches from the field of data mining. We develop three data mining-inspired evaluation approaches for discovered process models, one based on Cross Validation (CV), one based on the Minimal Description Length (MDL) principle, and one using methods based on Hidden Markov Models (HMMs). We conclude that process model evaluation faces similar yet different challenges compared to traditional data mining. Additional challenges emerge from the sequential nature of the data and the higher-level process models, which include concurrent dynamic behavior (Chapter 5). Finally, we point out current shortcomings and identify general challenges for conformance checking techniques. These challenges relate to the applicability of the conformance metric, the metric quality, and the bridging of different process modeling languages. We develop a flexible, language-independent conformance checking approach that provides a starting point to effectively address these challenges (Chapter 6). In Part III, we develop a concrete extension approach, provide a general model for process extensions, and apply our approach for the creation of simulation models. First, we develop a Petri-net based decision mining approach that aims at the discovery of decision rules at process choice points based on data attributes in the event log. While we leverage classification techniques from the data mining domain to actually infer the rules, we identify the challenges that relate to the initial formulation of the learning problem from a process perspective. We develop a simple approach to partially overcome these challenges, and we apply it in a case study (Chapter 7). Then, we develop a general model for process extensions to create integrated models including process, data, time, and resource perspective.We develop a concrete representation based on Coloured Petri-nets (CPNs) to implement and deploy this model for simulation purposes (Chapter 8). Finally, we evaluate the quality of automatically discovered simulation models in two case studies and extend our approach to allow for operational decision making by incorporating the current process state as a non-empty starting point in the simulation (Chapter 9). Chapter 10 concludes this thesis with a detailed summary of the contributions and a list of limitations and future challenges. The work presented in this dissertation is supported and accompanied by concrete implementations, which have been integrated in the ProM and ProMimport frameworks. Appendix A provides a comprehensive overview about the functionality of the developed software. The results presented in this dissertation have been presented in more than twenty peer-reviewed scientific publications, including several high-quality journals

An association rule dynamics and classification approach to event detection and tracking in Twitter.

Twitter is a microblogging application used for sending and retrieving instant on-line messages of not more than 140 characters. There has been a surge in Twitter activities since its launch in 2006 as well as steady increase in event detection research on Twitter data (tweets) in recent years. With 284 million monthly active users Twitter has continued to grow both in size and activity. The network is rapidly changing the way global audience source for information and influence the process of journalism [Newman, 2009]. Twitter is now perceived as an information network in addition to being a social network. This explains why traditional news media follow activities on Twitter to enhance their news reports and news updates. Knowing the significance of the network as an information dissemination platform, news media subscribe to Twitter accounts where they post their news headlines and include the link to their on-line news where the full story may be found. Twitter users in some cases, post breaking news on the network before such news are published by traditional news media. This can be ascribed to Twitter subscribers' nearness to location of events. The use of Twitter as a network for information dissemination as well as for opinion expression by different entities is now common. This has also brought with it the issue of computational challenges of extracting newsworthy contents from Twitter noisy data. Considering the enormous volume of data Twitter generates, users append the hashtag (#) symbol as prefix to keywords in tweets. Hashtag labels describe the content of tweets. The use of hashtags also makes it easy to search for and read tweets of interest. The volume of Twitter streaming data makes it imperative to derive Topic Detection and Tracking methods to extract newsworthy topics from tweets. Since hashtags describe and enhance the readability of tweets, this research is developed to show how the appropriate use of hashtags keywords in tweets can demonstrate temporal evolvements of related topic in real-life and consequently enhance Topic Detection and Tracking on Twitter network. We chose to apply our method on Twitter network because of the restricted number of characters per message and for being a network that allows sharing data publicly. More importantly, our choice was based on the fact that hashtags are an inherent component of Twitter. To this end, the aim of this research is to develop, implement and validate a new approach that extracts newsworthy topics from tweets' hashtags of real-life topics over a specified period using Association Rule Mining. We termed our novel methodology Transaction-based Rule Change Mining (TRCM). TRCM is a system built on top of the Apriori method of Association Rule Mining to extract patterns of Association Rules changes in tweets hashtag keywords at different periods of time and to map the extracted keywords to related real-life topic or scenario. To the best of our knowledge, the adoption of dynamics of Association Rules of hashtag co-occurrences has not been explored as a Topic Detection and Tracking method on Twitter. The application of Apriori to hashtags present in tweets at two consecutive period t and t + 1 produces two association rulesets, which represents rules evolvement in the context of this research. A change in rules is discovered by matching every rule in ruleset at time t with those in ruleset at time t + 1. The changes are grouped under four identified rules namely 'New' rules, 'Unexpected Consequent' and 'Unexpected Conditional' rules, 'Emerging' rules and 'Dead' rules. The four rules represent different levels of topic real-life evolvements. For example, the emerging rule represents very important occurrence such as breaking news, while unexpected rules represents unexpected twist of event in an on-going topic. The new rule represents dissimilarity in rules in rulesets at time t and t+1. Finally, the dead rule represents topic that is no longer present on the Twitter network. TRCM revealed the dynamics of Association Rules present in tweets and demonstrates the linkage between the different types of rule dynamics to targeted real-life topics/events. In this research, we conducted experimental studies on tweets from different domains such as sports and politics to test the performance effectiveness of our method. We validated our method, TRCM with carefully chosen ground truth. The outcome of our research experiments include: Identification of 4 rule dynamics in tweets' hashtags namely: New rules, Emerging rules, Unexpected rules and 'Dead' rules using Association Rule Mining. These rules signify how news and events evolved in real-life scenario. Identification of rule evolvements on Twitter network using Rule Trend Analysis and Rule Trace. Detection and tracking of topic evolvements on Twitter using Transaction-based Rule Change Mining TRCM. Identification of how the peculiar features of each TRCM rules affect their performance effectiveness on real datasets