Computing and Information Systems - Research Publications
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ItemRobidium: Automated synthesis of robotic process automation scripts from UI logs(CEUR Workshop Proceedings, 2020-01-01)This paper presents Robidium: a tool that discovers au- tomatable routine tasks from User Interactions (UI) logs and generates Robotic Process Automation (RPA) scripts to automate such routines. Unlike record-and-replay features provided by commercial RPA tools, Robidium may take as input an UI log that is not specifically recorded to capture a pre-identified task. Instead, the log may contain mixtures of automatable and non-automatable routines, interspersed with events that are not part of any routine as well as redundant or irrelevant events.
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ItemAutomated Discovery of Data Transformations for Robotic Process Automation(AAAI Press, 2020)Robotic Process Automation (RPA) is a technology for automating repetitive routines consisting of sequences of user interactions with one or more applications. In order to fully exploit the opportunities opened by RPA, companies need to discover which specific routines may be automated, and how. In this setting, this paper addresses the problem of analyzing User Interaction (UI) logs in order to discover routines where a user transfers data from one spreadsheet or (Web) form to another. The paper maps this problem to that of discovering data transformations by example - a problem for which several techniques are available. The paper shows that a naive application of a state-of-the-art technique for data transformation discovery is computationally inefficient. Accordingly, the paper proposes two optimizations that take advantage of the information in the UI log and the fact that data transfers across applications typically involve copying alphabetic and numeric tokens separately. The proposed approach and its optimizations are evaluated using UI logs that replicate a real-life repetitive data transfer routine.
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ItemIdentifying candidate routines for Robotic Process Automation from unsegmented UI logs(IEEE, 2020-10-22)Robotic Process Automation (RPA) is a technology to develop software bots that automate repetitive sequences of interactions between users and software applications (a.k. a. routines). To take full advantage of this technology, organizations need to identify and to scope their routines. This is a challenging endeavor in large organizations, as routines are usually not concentrated in a handful of processes, but rather scattered across the process landscape. Accordingly, the identification of routines from User Interaction (UI) logs has received significant attention. Existing approaches to this problem assume that the UI log is segmented, meaning that it consists of traces of a task that is presupposed to contain one or more routines. However, a UI log usually takes the form of a single unsegmented sequence of events. This paper presents an approach to discover candidate routines from unsegmented UI logs in the presence of noise, i.e. events within or between routine instances that do not belong to any routine. The approach is implemented as an open-source tool and evaluated using synthetic and real-life UI logs.
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ItemAutomated discovery of declarative process models with correlated data conditions(Elsevier, 2020-03-01)Automated process discovery techniques enable users to generate business process models from event logs extracted from enterprise information systems. Traditional techniques in this field generate procedural process models (e.g., in the BPMN notation). When dealing with highly variable processes, the resulting procedural models are often too complex to be practically usable. An alternative approach is to discover declarative process models, which represent the behavior of the process as a set of constraints. Declarative process discovery techniques have been shown to produce simpler models than procedural ones, particularly for processes with high variability. However, the bulk of approaches for automated discovery of declarative process models focus on the control-flow perspective, ignoring the data perspective. This paper addresses the problem of discovering declarative process models with data conditions. Specifically, the paper tackles the problem of discovering constraints that involve two activities of the process such that each of these two activities is associated with a condition that must hold when the activity occurs. The paper presents and compares two approaches to the problem of discovering such conditions. The first approach uses clustering techniques in conjunction with a rule mining technique, while the second approach relies on redescription mining techniques. The two approaches (and their variants) are empirically compared using a combination of synthetic and real-life event logs. The experimental results show that the former approach outperforms the latter when it comes to re-discovering constraints artificially injected in a log. Also, the former approach is in most of the cases more computationally efficient. On the other hand, redescription mining discovers rules with higher confidence (and lower support) suggesting that it may be used to discover constraints that hold for smaller subsets of cases of a process.