Office Automation Systems that are “Programmed” by their Users

Bocionek, Siegfried; Mitchell, Tom M.

doi:10.1007/978-3-642-78486-6_32

Cited by 6 publications

(6 citation statements)

References 4 publications

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“…The main areas of interest concerned how to learn the structure of the graph that represents the process model. As mentioned previously, early approaches were based on traditional graph models such as FSMs [Cook and Wolf 1996;Bocionek and Mitchell 1993] or HMMs [Herbst 2000a], which are unable to model concurrency [Herbst and Karagiannis 1998;Herbst 2000b] and hence are useless in many real cases. Techniques able to handle more complex cases followed in subsequent years.…”

Section: Related Workmentioning

confidence: 99%

Incremental Learning of Daily Routines as Workflows in a Smart Home Environment

Carolis

Ferilli

Redavid

2015

ACM Trans. Interact. Intell. Syst.

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Smart home environments should proactively support users in their activities, anticipating their needs according to their preferences. Understanding what the user is doing in the environment is important for adapting the environment's behavior, as well as for identifying situations that could be problematic for the user. Enabling the environment to exploit models of the user's most common behaviors is an important step toward this objective. In particular, models of the daily routines of a user can be exploited not only for predicting his/her needs, but also for comparing the actual situation at a given moment with the expected one, in order to detect anomalies in his/her behavior. While manually setting up process models in business and factory environments may be cost-effective, building models of the processes involved in people's everyday life is infeasible. This fact fully justifies the interest of the Ambient Intelligence community in automatically learning such models from examples of actual behavior. Incremental adaptation of the models and the ability to express/learn complex conditions on the involved tasks are also desirable. This article describes how process mining can be used for learning users' daily routines from a dataset of annotated sensor data. The solution that we propose relies on a First-Order Logic learning approach. Indeed, First-Order Logic provides a single, comprehensive and powerful framework for supporting all the previously mentioned features. Our experiments, performed both on a proprietary toy dataset and on publicly available real-world ones, indicate that this approach is efficient and effective for learning and modeling daily routines in Smart Home Environments.

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Section: Related Workmentioning

confidence: 99%

Incremental Learning of Daily Routines as Workflows in a Smart Home Environment

Carolis

Ferilli

Redavid

2015

ACM Trans. Interact. Intell. Syst.

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“…A partially related topic, faced in [6], is learning software agents that provide assistance in managing work-related activities, called software secretaries, by a dialog-based interaction with a skilled user that explains the tasks he is performing. This is mapped onto the task of learning from scratch a FSM representing the net of negotiations between agents.…”

Section: Miningmentioning

confidence: 99%

A Logic Framework for Incremental Learning of Process Models

Ferilli

Esposito

2013

Fundamenta Informaticae

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Standardized processes are important for correctly carrying out activities in an organization. Often the procedures they describe are already in operation, and the need is to understand and formalize them in a model that can support their analysis, replication and enforcement. Manually building these models is complex, costly and error-prone. Hence, the interest in automatically learning them from examples of actual procedures. Desirable options are incrementality in learning and adapting the models, and the ability to express triggers and conditions on the tasks that make up the workflow. This paper proposes a framework based on First-Order Logic that solves many shortcomings of previous approaches to this problem in the literature, allowing to deal with complex domains in a powerful and flexible way. Indeed, First-Order Logic provides a single, comprehensive and expressive representation and manipulation environment for supporting all of the above requirements. A purposely devised experimental evaluation confirms the effectiveness and efficiency of the proposed solution.processes and to enact them. The availability of a suitable process model can determine production and economic success of a company. While most information for properly setting up these models comes from the practitioners that day by day carry out and improve the procedures, formal models of activities are typically produced by supervisors and managers, a gap that often causes models not to perfectly fit the practice. Additionally, producing the models is in itself a complex, costly and error-prone activity [22]. A related problem is the need of adapting and fine-tuning existing models, when available, in dynamic environments (approached in [14] by allowing modifications to the workflow model). Hence, the strong motivation to study processes and techniques for modeling them, and the need of tools that can (at least partially) automatically learn them. For instance, companies that do not have a process management yet can obtain this way an initial model from examples of current practice, to be later refined and finetuned by experts; on the other hand, companies that have developed an abstract model can compare it to the current practice, or exploit it to supervise subsequent process executions or to generate simulated executions and check their correctness or compliance to their policy.In the following, we present a novel approach to learning process models in a perspective of strict comparison to previous proposals, in order to show both their similarities and their differences. The rest of the paper is organized as follows. Section 2 introduces basic notions about the application field, and Section 3 describes previous approaches existing in the literature to tackle these problems. Then, the proposed solution (representational framework and inference techniques) is described in Section 4, also pointing out noteworthy features with respect to previous proposals. Section 5 discusses possible exploitations of the learned models, and Section 6 re...

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“…It uses a technique called dialog-based learning to acquire a workflow model represented as a finite state machine by observing the transfer of structured e-mails. RAP has some limitations concerning inconsistent user behavior (see [2]), which are not present in our approach.…”

Section: Related Workmentioning

confidence: 95%

“…Given a sequence of observations e = e 1 e 2 : : : e l with e i 2 V and an initial HMM 0 = Q; V; A 0 ; B 0 ; 0 the Baum-Welch algorithm iteratively re-estimates the parameters A i ; B i and i until some limiting point is reached. 2 The formulas for the reestimation of the parameters are given for example in [11]. It was proven that this procedure converges to a local maximum of the likelihood function P ej .…”

Section: Hidden Markov Modelsmentioning

confidence: 99%

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Integrating machine learning and workflow management to support acquisition and adaptation of workflow models

Herbst

Karagiannis

Proceedings Ninth International Workshop on Database and Expert Systems Applications (Cat. No.98EX130)

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Current workflow management systems (WFMS) offer little aid for the acquisition of workflow models and their adaptation to changing requirements. To support these activities we propose to integrate machine learning and workflow management. This enables an inductive approach to workflow acquisition and adaptation by processing traces of manually enacted workflows. We present a machine learning component that combines two different machine learning algorithms. In this paper we focus mainly on the first one, which induces the structure of the workflow, based on the induction of hidden markov models. The second algorithm, a standard decision rule induction algorithm, induces transition conditions. The main concepts have been implemented in a prototype, which we have validated using artificial process traces. The induced workflow models can be imported by the business process management system ADONIS 1 .

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Office Automation Systems that are “Programmed” by their Users

Cited by 6 publications

References 4 publications

Incremental Learning of Daily Routines as Workflows in a Smart Home Environment

Incremental Learning of Daily Routines as Workflows in a Smart Home Environment

A Logic Framework for Incremental Learning of Process Models

Integrating machine learning and workflow management to support acquisition and adaptation of workflow models

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