A recursive paradigm for aligning observed behavior of large structured process models

The alignment of observed and modeled behavior is a crucial problem in process mining, since it opens the door for conformance checking and enhancement of process models. The state of the art techniques for the computation of alignments rely on a full exploration of the combination of the model state space and the observed behavior (an event log), which hampers their applicability for large instances. This paper presents a fresh view to the alignment problem: the computation of alignments is casted as the resolution of Integer Linear Programming models, where the user can decide the granularity of the alignment steps. Moreover, a novel recursive strategy is used to split the problem into small pieces, exponentially reducing the complexity of the ILP models to be solved. The contributions of this paper represent a promising alternative to fight the inherent complexity of computing alignments for large instances.Peer ReviewedPostprint (author's final draft

Process Mining for Six Sigma

Process mining offers a set of techniques for gaining data-based insights into business processes from event logs. The literature acknowledges the potential benefits of using process mining techniques in Six Sigma-based process improvement initiatives. However, a guideline that is explicitly dedicated on how process mining can be systematically used in Six Sigma initiatives is lacking. To address this gap, the Process Mining for Six Sigma (PMSS) guideline has been developed to support organizations in systematically using process mining techniques aligned with the DMAIC (Define-Measure-Analyze-Improve-Control) model of Six Sigma. Following a design science research methodology, PMSS and its tool support have been developed iteratively in close collaboration with experts in Six Sigma and process mining, and evaluated by means of focus groups, demonstrations and interviews with industry experts. The results of the evaluations indicate that PMSS is useful as a guideline to support Six Sigma-based process improvement activities. It offers a structured guideline for practitioners by extending the DMAIC-based standard operating procedure. PMSS can help increasing the efficiency and effectiveness of Six Sigma-based process improving efforts. This work extends the body of knowledge in the fields of process mining and Six Sigma, and helps closing the gap between them. Hence, it contributes to the broad field of quality management

FMAP: Distributed Cooperative Multi-Agent Planning

This paper proposes FMAP (Forward Multi-Agent Planning), a fully-distributed multi-agent planning method that integrates planning and coordination. Although FMAP is specifically aimed at solving problems that require cooperation among agents, the flexibility of the domain-independent planning model allows FMAP to tackle multi-agent planning tasks of any type. In FMAP, agents jointly explore the plan space by building up refinement plans through a complete and flexible forward-chaining partial-order planner. The search is guided by h D T G , a novel heuristic function that is based on the concepts of Domain Transition Graph and frontier state and is optimized to evaluate plans in distributed environments. Agents in FMAP apply an advanced privacy model that allows them to adequately keep private information while communicating only the data of the refinement plans that is relevant to each of the participating agents. Experimental results show that FMAP is a general-purpose approach that efficiently solves tightly-coupled domains that have specialized agents and cooperative goals as well as loosely-coupled problems. Specifically, the empirical evaluation shows that FMAP outperforms current MAP systems at solving complex planning tasks that are adapted from the International Planning Competition benchmarks.This work has been partly supported by the Spanish MICINN under projects Consolider Ingenio 2010 CSD2007-00022 and TIN2011-27652-C03-01, the Valencian Prometeo project II/2013/019, and the FPI-UPV scholarship granted to the first author by the Universitat Politecnica de Valencia.Torreño Lerma, A.; Onaindia De La Rivaherrera, E.; Sapena Vercher, O. (2014). FMAP: Distributed Cooperative Multi-Agent Planning. Applied Intelligence. 41(2):606-626. https://doi.org/10.1007/s10489-014-0540-2S606626412Benton J, Coles A, Coles A (2012) Temporal planning with preferences and time-dependent continuous costs. In: Proceedings of the 22nd international conference on automated planning and scheduling (ICAPS). AAAI, pp 2–10Borrajo D. (2013) Multi-agent planning by plan reuse. In: Proceedings of the 12th international conference on autonomous agents and multi-agent systems (AAMAS). IFAAMAS, pp 1141–1142Boutilier C, Brafman R (2001) Partial-order planning with concurrent interacting actions. J Artif Intell Res 14(105):136Brafman R, Domshlak C (2008) From one to many: planning for loosely coupled multi-agent systems. In: Proceedings of the 18th international conference on automated planning and scheduling (ICAPS). AAAI, pp 28–35Brenner M, Nebel B (2009) Continual planning and acting in dynamic multiagent environments. J Auton Agents Multiagent Syst 19(3):297–331Bresina J, Dearden R, Meuleau N, Ramakrishnan S, Smith D, Washington R (2002) Planning under continuous time and resource uncertainty: a challenge for AI. In: Proceedings of the 18th conference on uncertainty in artificial intelligence (UAI). Morgan Kaufmann, pp 77–84Cox J, Durfee E (2009) Efficient and distributable methods for solving the multiagent plan coordination problem. Multiagent Grid Syst 5(4):373–408Crosby M, Rovatsos M, Petrick R (2013) Automated agent decomposition for classical planning. In: Proceedings of the 23rd international conference on automated planning and scheduling (ICAPS). AAAI, pp 46–54Dimopoulos Y, Hashmi MA, Moraitis P (2012) μ-satplan: Multi-agent planning as satisfiability. Knowl-Based Syst 29:54–62Fikes R, Nilsson N (1971) STRIPS: a new approach to the application of theorem proving to problem solving. Artif Intell 2(3):189–208Gerevini A, Haslum P, Long D, Saetti A, Dimopoulos Y (2009) Deterministic planning in the fifth international planning competition: PDDL3 and experimental evaluation of the planners. Artif Intell 173(5-6):619–668Ghallab M, Nau D, Traverso P (2004) Automated planning. Theory and practice. Morgan KaufmannGünay A, Yolum P (2013) Constraint satisfaction as a tool for modeling and checking feasibility of multiagent commitments. Appl Intell 39(3):489–509Helmert M (2004) A planning heuristic based on causal graph analysis. In: Proceedings of the 14th international conference on automated planning and scheduling ICAPS. AAAI, pp 161–170Hoffmann J, Nebel B (2001) The FF planning system: fast planning generation through heuristic search. J Artif Intell Res 14:253–302Jannach D, Zanker M (2013) Modeling and solving distributed configuration problems: a CSP-based approach. IEEE Trans Knowl Data Eng 25(3):603–618Jonsson A, Rovatsos M (2011) Scaling up multiagent planning: a best-response approach. In: Proceedings of the 21st international conference on automated planning and scheduling (ICAPS). AAAI, pp 114–121Kala R, Warwick K (2014) Dynamic distributed lanes: motion planning for multiple autonomous vehicles. Appl Intell:1–22Koehler J, Ottiger D (2002) An AI-based approach to destination control in elevators. AI Mag 23(3):59–78Kovacs DL (2011) Complete BNF description of PDDL3.1. Technical reportvan der Krogt R (2009) Quantifying privacy in multiagent planning. Multiagent Grid Syst 5(4):451–469Kvarnström J (2011) Planning for loosely coupled agents using partial order forward-chaining. In: Proceedings of the 21st international conference on automated planning and scheduling (ICAPS). AAAI, pp 138–145Lesser V, Decker K, Wagner T, Carver N, Garvey A, Horling B, Neiman D, Podorozhny R, Prasad M, Raja A et al (2004) Evolution of the GPGP/TAEMS domain-independent coordination framework. Auton Agents Multi-Agent Syst 9(1–2):87–143Long D, Fox M (2003) The 3rd international planning competition: results and analysis. J Artif Intell Res 20:1–59Nissim R, Brafman R, Domshlak C (2010) A general, fully distributed multi-agent planning algorithm. In: Proceedings of the 9th international conference on autonomous agents and multiagent systems (AAMAS). IFAAMAS, pp 1323–1330O’Brien P, Nicol R (1998) FIPA - towards a standard for software agents. BT Tech J 16(3):51–59Öztürk P, Rossland K, Gundersen O (2010) A multiagent framework for coordinated parallel problem solving. Appl Intell 33(2):132–143Pal A, Tiwari R, Shukla A (2013) Communication constraints multi-agent territory exploration task. Appl Intell 38(3):357–383Richter S, Westphal M (2010) The LAMA planner: guiding cost-based anytime planning with landmarks. J Artif Intell Res 39(1):127–177de la Rosa T, García-Olaya A, Borrajo D (2013) A case-based approach to heuristic planning. Appl Intell 39(1):184–201Sapena O, Onaindia E (2008) Planning in highly dynamic environments: an anytime approach for planning under time constraints. Appl Intell 29(1):90–109Sapena O, Onaindia E, Garrido A, Arangú M (2008) A distributed CSP approach for collaborative planning systems. Eng Appl Artif Intell 21(5):698–709Serrano E, Such J, Botía J, García-Fornes A (2013) Strategies for avoiding preference profiling in agent-based e-commerce environments. Appl Intell:1–16Smith D, Frank J, Jónsson A (2000) Bridging the gap between planning and scheduling. Knowl Eng Rev 15(1):47–83Such J, García-Fornes A, Espinosa A, Bellver J (2012) Magentix2: a privacy-enhancing agent platform. Eng Appl Artif Intell:96–109Tonino H, Bos A, de Weerdt M, Witteveen C (2002) Plan coordination by revision in collective agent based systems. Artif Intell 142(2):121–145Torreño A, Onaindia E, Sapena O (2012) An approach to multi-agent planning with incomplete information. In: Proceedings of the 20th European conference on artificial intelligence (ECAI), vol 242. IOS Press, pp 762–767Torreño A, Onaindia E, Sapena O (2014) A flexible coupling approach to multi-agent planning under incomplete information. Knowl Inf Syst 38(1):141–178Van Der Krogt R, De Weerdt M (2005) Plan repair as an extension of planning. In: Proceedings of the 15th international conference on automated planning and scheduling (ICAPS). AAAI, pp 161–170de Weerdt M, Clement B (2009) Introduction to planning in multiagent systems. Multiagent Grid Syst 5(4):345– 355Yokoo M, Durfee E, Ishida T, Kuwabara K (1998) The distributed constraint satisfaction problem: formalization and algorithms. IEEE Trans Knowl Data Eng 10(5):673–685Zhang J, Nguyen X, Kowalczyk R (2007) Graph-based multi-agent replanning algorithm. In: Proceedings of the 6th international joint conference conference on autonomous agents and multiagent systems (AAMAS). IFAAMAS, pp 798–80

Data-Driven Process Discovery - Revealing Conditional Infrequent Behavior from Event Logs

Process discovery methods automatically infer process models from event logs. Often, event logs contain so-called noise, e.g., infrequent outliers or recording errors, which obscure the main behavior of the process. Existing methods filter this noise based on the frequency of event labels: infrequent paths and activities are excluded. However, infrequent behavior may reveal important insights into the process. Thus, not all infrequent behavior should be considered as noise. This paper proposes the Data-aware Heuristic Miner (DHM), a process discovery method that uses the data attributes to distinguish infrequent paths from random noise by using classification techniques. Data- and control-flow of the process are discovered together. We show that the DHM is, to some degree, robust against random noise and reveals data-driven decisions, which are filtered by other discovery methods. The DHM has been successfully tested on several real-life event logs, two of which we present in this paper

Generalized alignment-based trace clustering of process behavior

Process mining techniques use event logs containing real process executions in order to mine, align and extend process models. The partition of an event log into trace variants facilitates the understanding and analysis of traces, so it is a common pre-processing in process mining environments. Trace clustering automates this partition; traditionally it has been applied without taking into consideration the availability of a process model. In this paper we extend our previous work on process model based trace clustering, by allowing cluster centroids to have a complex structure, that can range from a partial order, down to a subnet of the initial process model. This way, the new clustering framework presented in this paper is able to cluster together traces that are distant only due to concurrency or loop constructs in process models. We show the complexity analysis of the different instantiations of the trace clustering framework, and have implemented it in a prototype tool that has been tested on different datasets.Peer ReviewedPostprint (author's final draft

Blood Flow and Glucose Metabolism in Stage IV Breast Cancer: Heterogeneity of Response During Chemotherapy

Objective: The purpose of the study was to compare early changes in blood flow (BF) and glucose metabolism (MRglu) in metastatic breast cancer lesions of patients treated with chemotherapy. Methods: Eleven women with stage IV cancer and lesions in breast, lymph nodes, liver, and bone were scanned before treatment and after the first course of chemotherapy. BF, distribution volume of water (Vd), MRglu/BF ratio, MRgluand its corresponding rate constants K1and k3were compared per tumor lesion before and during therapy. Results: At baseline, mean BF and MRgluvaried among different tumor lesions, but mean Vdwas comparable in all lesions. After one course of chemotherapy, mean MRgludecreased in all lesions. Mean BF decreased in breast and node lesions and increased in bone lesions. Vddecreased in breast and nodes, but did not change in bone lesions. The MRglu/BF ratio decreased in breast and bone lesions and increased in node lesions. In patients with multiple tumor lesions BF and MRgluresponse could be very heterogeneous, even within similar types of metastases. BF and MRgluincreased in lesions of patients who experienced early disease progression or showed no response during clinical follow-up. Conclusion: BF and MRgluchanges separately give unique information on different aspects of tumor response to chemotherapy. Changes in BF and MRgluparameters can be remarkably heterogeneous in patients with multiple lesions

Phase II study of carfilzomib, thalidomide, and low-dose dexamethasone as induction and consolidation in newly diagnosed, transplant eligible patients with multiple myeloma; the Carthadex trial

This is a phase II dose escalation trial of carfilzomib in combination with thalidomide and dexamethasone for induction and consolidation in transplant-eligible patients with newly diagnosed multiple myeloma (NDMM). The results of four dose levels are reported. Induction therapy consisted of four cycles of carfilzomib 20/27 mg/m2 (n=50), 20/36 mg/m2 (n=20), 20/45 mg/m2 (n=21), and 20/56 mg/m2 (n=20) on days 1, 2, 8, 9, 15, 16 of a 28-day cycle; thalidomide 200 mg on day 1 through 28 and dexamethasone 40 mg weekly. Induction therapy was followed by high-dose melphalan and autologous stem cell transplantation and consolidation therapy with four cycles of carfilzomib, thalidomide and dexamethasone in the same schedule except a lower dose of thalidomide (50 mg). Very good partial response rate or better and complete response rate or better after ind