Presentation of the paper entitled “Semantical Vacuity Detection in Declarative Process Mining”
(http://dx.doi.org/10.1007/978-3-319-45348-4_10), held at BPM 2016, Rio de Janeiro, Brazil (http://bpm2016.uniriotec.br/).
A large share of the literature on process mining based on declarative process modeling languages, like DECLARE, relies on the notion of constraint activation to distinguish between the case in which a process execution recorded in event data “vacuously” satisfies a constraint, or satisfies the constraint in an “interesting way”. This fine-grained indicator is then used to decide whether a candidate constraint supported by the analyzed event log is indeed relevant or not. Unfortunately, this notion of relevance has never been formally defined, and all the proposals existing in the literature use ad-hoc definitions that are only applicable to a pre-defined set of constraint patterns. This makes existing declarative process mining technique inapplicable when the target constraint language is extensible and may contain formulae that go beyond pre-defined patterns. In this paper, we tackle this hot, open challenge and show how the notion of constraint activation and vacuous satisfaction can be captured semantically, in the case of constraints expressed in arbitrary temporal logics over finite traces. We then extend the standard automata-based approach so as to incorporate relevance-related information. We finally report on an implementation and experimentation of the approach that confirms the advantages and feasibility of our solution.
Slides of the presentation held at the Humboldt University of Berlin on 2016, December the 7th.
Abstract:
The declarative modelling of business processes is based upon the specification of behavioural rules that constrain the work-flows enactment. It is meant not to explicitly specify every possible execution path from the beginning to the end: The carry-out of the process is up to the actors, who can vary the execution dynamics as long as they do not violate the constraints imposed by the declarative model. The constraints specify the conditions that require or forbid the execution of activities, either considering them singularly or depending on the occurrence of other ones. In this talk, the recent advancements in the automated discovery of declarative control flows from event logs are discussed, together with open challenges in the field.
Business process model collections are fundamental organisational assets as they inform strategic decision-making and drive system implementation. This presentation tackles the challenge of keeping these collections current and relevant in today’s volatile corporate environment. It does so by illustrating a new research direction for the development of "liquid process model collections", which borrows from the areas of process mining and management of large process model collections.
In our last paper we built the Load statements and encryption to enable our building project to learn about Secant Theory in both Digital and advancing to Ark Mode. We have begun to devise the New Schema to move into Ark Mode and the crossover requirements. We demonstrate what the move from one secant wheel accomplished for Product Media in metric terms, and in V2 will examine Secant Multi-Wheel Applications and in V3 learn to drive Instances
In our last paper we built the Load statements and encryption to enable our building project to learn about Secant Theory in both Digital and advancing to Ark Mode. We have begun to devise the New Schema to move into Ark Mode and the crossover requirements. We demonstrate what the move from one secant wheel accomplished for Product Media in metric terms, and in V2 will examine Secant Multi-Wheel Applications and in V3 learn to drive Instances
Ark in Glass (V4) Summary Concepts in Secant Wheel ConstructionBrij Consulting, LLC
In our last paper "the New Stone" we built the Load statements and encryption to enable our building project to learn about Secant Theory in both Digital and advancing to Ark Mode. We have begun to devise the New Schema to move into Ark Mode and the crossover requirements. We demonstrate what the move from one secant wheel accomplished for Product Media in metric terms, and in V2 will examine Secant Multi-Wheel Applications and in V3 learn to drive Instances; In V4 We have added statements for Change in Economic Position and Load Comparison to demonstrate the economic viability of the Method. Also there is a table of Secant Wheel Factual Data and Comparative information.
Look but don’t touch: On the impalpable bond between blockchain and processClaudio Di Ciccio
Slides of the keynote held at the BPM Blockchain Forum 2023, 13 September 2023, Utrecht, Netherlands.
Synopsis:
Multi-party business processes rely on the collaboration of various players in a decentralized setting. Blockchain technology can facilitate the automation of these processes, even in cases where trust among participants is limited. Transactions are stored in a ledger, a replica of which is retained by every node of the blockchain network. The operations saved thereby are thus publicly accessible, which benefits transparency, reliability, and persistence. Smart contracts can encode the system behavior agreed upon by the involved parties to define the behaviour of collaborative processes. Rule enforcement, traceability and non-repudiation are thus catered for, too. However, data, objects and services in the outer world are not directly accessible from within a blockchain execution evironment. On one hand, access to limited information hinders the adoption of programmable blockchains as an effective aid to process intelligence. On the other hand, transferring every bit of off-chain information on-chain is not only impractical but also undesirable, as this operation could violate typical confidentiality requirements in enterprise settings. In this talk, we discuss and explore approaches aimed at strengthening the bond between process and blockchain execution environments, balancing between knowledge sharing and secrecy preservation.
Slides of the presentation held at the Humboldt University of Berlin on 2016, December the 7th.
Abstract:
The declarative modelling of business processes is based upon the specification of behavioural rules that constrain the work-flows enactment. It is meant not to explicitly specify every possible execution path from the beginning to the end: The carry-out of the process is up to the actors, who can vary the execution dynamics as long as they do not violate the constraints imposed by the declarative model. The constraints specify the conditions that require or forbid the execution of activities, either considering them singularly or depending on the occurrence of other ones. In this talk, the recent advancements in the automated discovery of declarative control flows from event logs are discussed, together with open challenges in the field.
Business process model collections are fundamental organisational assets as they inform strategic decision-making and drive system implementation. This presentation tackles the challenge of keeping these collections current and relevant in today’s volatile corporate environment. It does so by illustrating a new research direction for the development of "liquid process model collections", which borrows from the areas of process mining and management of large process model collections.
In our last paper we built the Load statements and encryption to enable our building project to learn about Secant Theory in both Digital and advancing to Ark Mode. We have begun to devise the New Schema to move into Ark Mode and the crossover requirements. We demonstrate what the move from one secant wheel accomplished for Product Media in metric terms, and in V2 will examine Secant Multi-Wheel Applications and in V3 learn to drive Instances
In our last paper we built the Load statements and encryption to enable our building project to learn about Secant Theory in both Digital and advancing to Ark Mode. We have begun to devise the New Schema to move into Ark Mode and the crossover requirements. We demonstrate what the move from one secant wheel accomplished for Product Media in metric terms, and in V2 will examine Secant Multi-Wheel Applications and in V3 learn to drive Instances
Ark in Glass (V4) Summary Concepts in Secant Wheel ConstructionBrij Consulting, LLC
In our last paper "the New Stone" we built the Load statements and encryption to enable our building project to learn about Secant Theory in both Digital and advancing to Ark Mode. We have begun to devise the New Schema to move into Ark Mode and the crossover requirements. We demonstrate what the move from one secant wheel accomplished for Product Media in metric terms, and in V2 will examine Secant Multi-Wheel Applications and in V3 learn to drive Instances; In V4 We have added statements for Change in Economic Position and Load Comparison to demonstrate the economic viability of the Method. Also there is a table of Secant Wheel Factual Data and Comparative information.
Look but don’t touch: On the impalpable bond between blockchain and processClaudio Di Ciccio
Slides of the keynote held at the BPM Blockchain Forum 2023, 13 September 2023, Utrecht, Netherlands.
Synopsis:
Multi-party business processes rely on the collaboration of various players in a decentralized setting. Blockchain technology can facilitate the automation of these processes, even in cases where trust among participants is limited. Transactions are stored in a ledger, a replica of which is retained by every node of the blockchain network. The operations saved thereby are thus publicly accessible, which benefits transparency, reliability, and persistence. Smart contracts can encode the system behavior agreed upon by the involved parties to define the behaviour of collaborative processes. Rule enforcement, traceability and non-repudiation are thus catered for, too. However, data, objects and services in the outer world are not directly accessible from within a blockchain execution evironment. On one hand, access to limited information hinders the adoption of programmable blockchains as an effective aid to process intelligence. On the other hand, transferring every bit of off-chain information on-chain is not only impractical but also undesirable, as this operation could violate typical confidentiality requirements in enterprise settings. In this talk, we discuss and explore approaches aimed at strengthening the bond between process and blockchain execution environments, balancing between knowledge sharing and secrecy preservation.
Measurement of Rule-based LTLf Declarative Process SpecificationsClaudio Di Ciccio
Slides of the paper presented at the 4th Int. Conference on Process Mining (ICPM 2022, Bolzano, Italy).
Synopsis:
The classical checking of declarative Linear Temporal Logic on Finite Traces (LTLf) specifications verifies whether conjunctions of sets of formulae are satisfied by collections of finite traces. The data on which the verification is conducted may be corrupted by a number of logging errors or execution deviations at the level of single elements within a trace. The ability to quantitatively assess the extent to which traces satisfy a process specification (and not only if they do so or not at all) is thus key, especially in process mining scenarios. Previous techniques proposed for this aim either require formulae to be extended with quantitative operators or cater to the coarse granularity of whole traces. In this paper, we propose a framework to devise probabilistic measures for declarative process specifications on traces at the level of events, inspired by association rule mining. Thereupon, we describe a technique that measures the degree of satisfaction of these specifications over bags of traces. To assess our approach, we conduct an evaluation with real-world data.
Blockchain and smart contracts: infrastructure and platformsClaudio Di Ciccio
An introductory presentation on the main concepts of blockchain technologies, with a special focus on the smart contracts. The slides supported the talk held at the Cyber 4.0 Seminar on Cyber 4.0 Seminar on “Blockchain and Smart Contracts: Concepts and applications” on 2021-03-03, virtually hosted by the Sapienza University of Rome for the Cyber 4.0 Competence Centre.
Presented at the 12th International Conference on Business Process Management (BPM 2014), 7-11 September 2014, Eindhoven, The Netherlands.
Abstract: Process discovery is the task of generating models from event logs. Mining processes that operate in an environment of high variability is an ongoing research challenge because various algorithms tend to produce spaghetti-like models. This is particularly the case when procedural models are generated. A promising direction to tackle this challenge is the usage of declarative process modelling languages like Declare, which summarise complex behaviour in a compact set of behavioural constraints. However, Declare constraints with branching are expensive to be calculated.In addition, it is often the case that hundreds of branching Declare constraints are valid for the same log, thus making, again, the discovery results unreadable. In this paper, we address these problems from a theoretical angle. More specifically, we define the class of Target- Branched Declare constraints and investigate the formal properties it exhibits. Furthermore, we present a technique for the efficient discovery of compact Target-Branched Declare models. We discuss the merits of our work through an evaluation based on a prototypical implementation using both artificial and real-world event logs.
Introduction to the declarative specification of processesClaudio Di Ciccio
This slides deck contains a short introduction to the declarative specification of processes, with examples of how to describe a process with the Declare language.
Declarative Specification of Processes: Discovery and ReasoningClaudio Di Ciccio
A process describes the temporal evolution of a system. Capturing the rules that govern its control flow helps to understand the boundaries of its behaviour. The declarative specification of processes is based on the representation of those boundaries by means of constraints rooted in temporal logics. The execution dynamics can vary as long as they do not violate such constraints, which specify the conditions that require or forbid the execution of actions.
This talk revolves around the recent advancements in research concerning the discovery of, and reasoning on, the declarative specifications of processes. The discourse will include a focus on how to automatically extract the constraints from process data, and how to losslessly minimise the size of discovered constraint sets. The conclusion will illustrate open challenges and future research avenues in the field.
Extracting Event Logs for Process Mining from Data Stored on the BlockchainClaudio Di Ciccio
Presentation of the paper presented at the 2nd International Workshop on Security and Privacy-enhanced Business Process Management (SPBP’19), 2 September 2019, Vienna, Austria (pre-print available at https://easychair.org/publications/preprint/cW8l).
Abstract: The integration of business process management with blockchains across organisational borders provides a means to establish transparency of execution and auditing capabilities. To enable process analytics, though, non-trivial extraction and transformation tasks are necessary on the raw data stored in the ledger. In this paper, we describe our approach to retrieve process data from an Ethereum blockchain ledger and subsequently convert those data into an event log formatted according to the IEEE Extensible Event Stream (XES) standard. We show a proof-of-concept software artefact and its application on a data set produced by the smart contracts of a process execution engine stored on the public Ethereum blockchain network.
A blockchain can be defined as an immutable distributed ledger on which transactions exchanged between peers are recorded. Transactions are cryptographically signed and are meant to transfer digital commodities between parties. Lately, the blockchains have undergone a paradigm shift from mere electronic cash systems to a universal platform endowed with internal programming languages, on top of which decentralised applications can be built. That has been the turning point enabling the execution of inter-organisational business processes on blockchains.
In this talk, the concepts behind and around blockchains will be described, together with the current research and future directions on its usage as an infrastructure for business process management.
Blockchain based traceability of inter-organisational business processesClaudio Di Ciccio
Presentation of the paper entitled “Blockchain-based Traceability of Interorganisational Business Processes” (http://dx.doi.org/10.1007/978-3-319-94214-8_4), held at BMSD 2018, Vienna, Austria (http://www.is-bmsd.org/).
Abstract:
The blockchain technology opens up new opportunities for Business Process Management. This is mainly due to its unprecedented capability to let transactions be automatically executed and recorded by Smart Contracts in multi-peer environments, in a decentralised fashion and without central authoritative players to govern the workflow. In this way, blockchains also provide traceability. Traceability of information plays a pivotal role particularly in those supply chains where multiple parties are involved and rigorous criteria must be fulfilled to lead to a successful outcome. In this paper, we investigate how to run a business process in the context of a supply chain on a blockchain infrastructure so as to provide full traceability of its run-time enactment. Our approach retrieves information to trace process instances execution solely from the transactions written on-chain. To do so, hash-codes are reverseengineered based on the Solidity Smart Contract encoding of the generating process. We show the results of our investigation by means of an implemented software prototype, with a case study on the reportedly challenging context of the pharmaceutical supply chain.
Log-Based Understanding of Business Processes through Temporal Logic Query Ch...Claudio Di Ciccio
Process mining is a discipline that aims at discovering, monitoring and improving real-life processes by extracting knowledge from event logs. Process discovery and conformance checking are the two main process mining tasks. Process discovery techniques can be used to learn a process model from example traces in an event log, whereas the goal of conformance checking is to compare the observed behavior in the event log with the modeled behavior. In this paper, we propose an approach based on temporal logic query checking, which is in the middle between process discovery and conformance checking. It can be used to discover those LTL-based business rules that are valid in the log, by checking against the log a (user-defined) class of rules. The proposed approach is not limited to provide a boolean answer about the validity of a business rule in the log, but it rather provides valuable diagnostics in terms of traces in which the rule is satisfied (witnesses) and traces in which the rule is violated (counterexamples). We have implemented our approach as a proof of concept and conducted a wide experimentation using both synthetic and real-life logs.
Resolving Inconsistencies and Redundancies in Declarative Process ModelsClaudio Di Ciccio
Presentation of the article entitled “Semantical Vacuity Detection in Declarative Process Mining”
(https://doi.org/10.1016/j.is.2016.09.005), held at EMISA 2017, Essen, Germany (https://www2.informatik.hu-berlin.de/emisa2017/).
Declarative process models are specifications of workflows based on constraints. Any sequence of activities is allowed, as long as the constraints are not violated. To discover declarative models out of IT systems’ logs, existing techniques verify every possible constraint candidate against the recorded data. Those that hold true are included in the resulting model. A first issue is that some returned constraints can contradict one another, with the result that the model does not accept any execution and turns out to be unusable. A second challenge is the reduction of returned constraints to a minimum set of significant ones, for the sake of readability. Due to their computational complexity, none of those issues had been successfully tackled in the past. Our paper formally frames these problems and formulates an algorithmic solution for both. Its validity and efficiency are demonstrated by extensive experiments on real-world data.
Detecting Flight Trajectory Anomalies and Predicting Diversions in Freight Tr...Claudio Di Ciccio
Presentation of the paper entitled “Detecting Flight Trajectory Anomalies and Predicting Diversions in Freight Transportation”
(http://dx.doi.org/10.1016/j.dss.2016.05.004), held at EMISA 2016, Vienna, Austria (https://aic.ai.wu.ac.at/emisa2016/).
Abstract:
Timely identifying flight diversions is a crucial aspect of efficient multi-modal transportation. When an airplane diverts, logistics providers must promptly adapt their transportation plans in order to ensure proper delivery despite such an unexpected event. In practice, the different parties in a logistics chain do not exchange real-time information related to flights. This calls for a means to detect diversions that just requires publicly available data, thus being independent of the communication between different parties. The dependence on public data results in a challenge to detect anomalous behavior without knowing the planned flight trajectory. Our work addresses this challenge by introducing a prediction model that just requires information on an airplane’s position, velocity, and intended destination. This information is used to distinguish between regular and anomalous behavior. When an airplane displays anomalous behavior for an extended period of time, the model predicts a diversion. A quantitative evaluation shows that this approach is able to detect diverting airplanes with excellent precision and recall even without knowing planned trajectories as required by related research. By utilizing the proposed prediction model, logistics companies gain a significant amount of response time for these cases.
Ensuring Model Consistency in Declarative Process DiscoveryClaudio Di Ciccio
Presentation of the paper entitled “Ensuring Model Consistency in Declarative Process Discovery” (http://dx.doi.org/10.1007/978-3-319-23063-4_9) at the 13th International Conference on Business Process Management (BPM 2015), Innsbruck, Austria.
The main theme is the description of an automated technique to detect inconsistencies within mined declarative process models.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Measurement of Rule-based LTLf Declarative Process SpecificationsClaudio Di Ciccio
Slides of the paper presented at the 4th Int. Conference on Process Mining (ICPM 2022, Bolzano, Italy).
Synopsis:
The classical checking of declarative Linear Temporal Logic on Finite Traces (LTLf) specifications verifies whether conjunctions of sets of formulae are satisfied by collections of finite traces. The data on which the verification is conducted may be corrupted by a number of logging errors or execution deviations at the level of single elements within a trace. The ability to quantitatively assess the extent to which traces satisfy a process specification (and not only if they do so or not at all) is thus key, especially in process mining scenarios. Previous techniques proposed for this aim either require formulae to be extended with quantitative operators or cater to the coarse granularity of whole traces. In this paper, we propose a framework to devise probabilistic measures for declarative process specifications on traces at the level of events, inspired by association rule mining. Thereupon, we describe a technique that measures the degree of satisfaction of these specifications over bags of traces. To assess our approach, we conduct an evaluation with real-world data.
Blockchain and smart contracts: infrastructure and platformsClaudio Di Ciccio
An introductory presentation on the main concepts of blockchain technologies, with a special focus on the smart contracts. The slides supported the talk held at the Cyber 4.0 Seminar on Cyber 4.0 Seminar on “Blockchain and Smart Contracts: Concepts and applications” on 2021-03-03, virtually hosted by the Sapienza University of Rome for the Cyber 4.0 Competence Centre.
Presented at the 12th International Conference on Business Process Management (BPM 2014), 7-11 September 2014, Eindhoven, The Netherlands.
Abstract: Process discovery is the task of generating models from event logs. Mining processes that operate in an environment of high variability is an ongoing research challenge because various algorithms tend to produce spaghetti-like models. This is particularly the case when procedural models are generated. A promising direction to tackle this challenge is the usage of declarative process modelling languages like Declare, which summarise complex behaviour in a compact set of behavioural constraints. However, Declare constraints with branching are expensive to be calculated.In addition, it is often the case that hundreds of branching Declare constraints are valid for the same log, thus making, again, the discovery results unreadable. In this paper, we address these problems from a theoretical angle. More specifically, we define the class of Target- Branched Declare constraints and investigate the formal properties it exhibits. Furthermore, we present a technique for the efficient discovery of compact Target-Branched Declare models. We discuss the merits of our work through an evaluation based on a prototypical implementation using both artificial and real-world event logs.
Introduction to the declarative specification of processesClaudio Di Ciccio
This slides deck contains a short introduction to the declarative specification of processes, with examples of how to describe a process with the Declare language.
Declarative Specification of Processes: Discovery and ReasoningClaudio Di Ciccio
A process describes the temporal evolution of a system. Capturing the rules that govern its control flow helps to understand the boundaries of its behaviour. The declarative specification of processes is based on the representation of those boundaries by means of constraints rooted in temporal logics. The execution dynamics can vary as long as they do not violate such constraints, which specify the conditions that require or forbid the execution of actions.
This talk revolves around the recent advancements in research concerning the discovery of, and reasoning on, the declarative specifications of processes. The discourse will include a focus on how to automatically extract the constraints from process data, and how to losslessly minimise the size of discovered constraint sets. The conclusion will illustrate open challenges and future research avenues in the field.
Extracting Event Logs for Process Mining from Data Stored on the BlockchainClaudio Di Ciccio
Presentation of the paper presented at the 2nd International Workshop on Security and Privacy-enhanced Business Process Management (SPBP’19), 2 September 2019, Vienna, Austria (pre-print available at https://easychair.org/publications/preprint/cW8l).
Abstract: The integration of business process management with blockchains across organisational borders provides a means to establish transparency of execution and auditing capabilities. To enable process analytics, though, non-trivial extraction and transformation tasks are necessary on the raw data stored in the ledger. In this paper, we describe our approach to retrieve process data from an Ethereum blockchain ledger and subsequently convert those data into an event log formatted according to the IEEE Extensible Event Stream (XES) standard. We show a proof-of-concept software artefact and its application on a data set produced by the smart contracts of a process execution engine stored on the public Ethereum blockchain network.
A blockchain can be defined as an immutable distributed ledger on which transactions exchanged between peers are recorded. Transactions are cryptographically signed and are meant to transfer digital commodities between parties. Lately, the blockchains have undergone a paradigm shift from mere electronic cash systems to a universal platform endowed with internal programming languages, on top of which decentralised applications can be built. That has been the turning point enabling the execution of inter-organisational business processes on blockchains.
In this talk, the concepts behind and around blockchains will be described, together with the current research and future directions on its usage as an infrastructure for business process management.
Blockchain based traceability of inter-organisational business processesClaudio Di Ciccio
Presentation of the paper entitled “Blockchain-based Traceability of Interorganisational Business Processes” (http://dx.doi.org/10.1007/978-3-319-94214-8_4), held at BMSD 2018, Vienna, Austria (http://www.is-bmsd.org/).
Abstract:
The blockchain technology opens up new opportunities for Business Process Management. This is mainly due to its unprecedented capability to let transactions be automatically executed and recorded by Smart Contracts in multi-peer environments, in a decentralised fashion and without central authoritative players to govern the workflow. In this way, blockchains also provide traceability. Traceability of information plays a pivotal role particularly in those supply chains where multiple parties are involved and rigorous criteria must be fulfilled to lead to a successful outcome. In this paper, we investigate how to run a business process in the context of a supply chain on a blockchain infrastructure so as to provide full traceability of its run-time enactment. Our approach retrieves information to trace process instances execution solely from the transactions written on-chain. To do so, hash-codes are reverseengineered based on the Solidity Smart Contract encoding of the generating process. We show the results of our investigation by means of an implemented software prototype, with a case study on the reportedly challenging context of the pharmaceutical supply chain.
Log-Based Understanding of Business Processes through Temporal Logic Query Ch...Claudio Di Ciccio
Process mining is a discipline that aims at discovering, monitoring and improving real-life processes by extracting knowledge from event logs. Process discovery and conformance checking are the two main process mining tasks. Process discovery techniques can be used to learn a process model from example traces in an event log, whereas the goal of conformance checking is to compare the observed behavior in the event log with the modeled behavior. In this paper, we propose an approach based on temporal logic query checking, which is in the middle between process discovery and conformance checking. It can be used to discover those LTL-based business rules that are valid in the log, by checking against the log a (user-defined) class of rules. The proposed approach is not limited to provide a boolean answer about the validity of a business rule in the log, but it rather provides valuable diagnostics in terms of traces in which the rule is satisfied (witnesses) and traces in which the rule is violated (counterexamples). We have implemented our approach as a proof of concept and conducted a wide experimentation using both synthetic and real-life logs.
Resolving Inconsistencies and Redundancies in Declarative Process ModelsClaudio Di Ciccio
Presentation of the article entitled “Semantical Vacuity Detection in Declarative Process Mining”
(https://doi.org/10.1016/j.is.2016.09.005), held at EMISA 2017, Essen, Germany (https://www2.informatik.hu-berlin.de/emisa2017/).
Declarative process models are specifications of workflows based on constraints. Any sequence of activities is allowed, as long as the constraints are not violated. To discover declarative models out of IT systems’ logs, existing techniques verify every possible constraint candidate against the recorded data. Those that hold true are included in the resulting model. A first issue is that some returned constraints can contradict one another, with the result that the model does not accept any execution and turns out to be unusable. A second challenge is the reduction of returned constraints to a minimum set of significant ones, for the sake of readability. Due to their computational complexity, none of those issues had been successfully tackled in the past. Our paper formally frames these problems and formulates an algorithmic solution for both. Its validity and efficiency are demonstrated by extensive experiments on real-world data.
Detecting Flight Trajectory Anomalies and Predicting Diversions in Freight Tr...Claudio Di Ciccio
Presentation of the paper entitled “Detecting Flight Trajectory Anomalies and Predicting Diversions in Freight Transportation”
(http://dx.doi.org/10.1016/j.dss.2016.05.004), held at EMISA 2016, Vienna, Austria (https://aic.ai.wu.ac.at/emisa2016/).
Abstract:
Timely identifying flight diversions is a crucial aspect of efficient multi-modal transportation. When an airplane diverts, logistics providers must promptly adapt their transportation plans in order to ensure proper delivery despite such an unexpected event. In practice, the different parties in a logistics chain do not exchange real-time information related to flights. This calls for a means to detect diversions that just requires publicly available data, thus being independent of the communication between different parties. The dependence on public data results in a challenge to detect anomalous behavior without knowing the planned flight trajectory. Our work addresses this challenge by introducing a prediction model that just requires information on an airplane’s position, velocity, and intended destination. This information is used to distinguish between regular and anomalous behavior. When an airplane displays anomalous behavior for an extended period of time, the model predicts a diversion. A quantitative evaluation shows that this approach is able to detect diverting airplanes with excellent precision and recall even without knowing planned trajectories as required by related research. By utilizing the proposed prediction model, logistics companies gain a significant amount of response time for these cases.
Ensuring Model Consistency in Declarative Process DiscoveryClaudio Di Ciccio
Presentation of the paper entitled “Ensuring Model Consistency in Declarative Process Discovery” (http://dx.doi.org/10.1007/978-3-319-23063-4_9) at the 13th International Conference on Business Process Management (BPM 2015), Innsbruck, Austria.
The main theme is the description of an automated technique to detect inconsistencies within mined declarative process models.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...
Semantical Vacuity Detection in Declarative Process Mining
1. Semantical Vacuity Detection in
Declarative Process Mining
Fabrizio Maria Maggi, Marco Montali, Claudio Di Ciccio and Jan Mendling
14th International Conference on Business Process Management
Rio de Janeiro, Brazil
claudio.di.ciccio@wu.ac.at
3. Prelude
Every unicorn is green
SEITE 3
)(. xxUnicorn )(xGreen
It is true. After all, have you ever seen a unicorn?
We thank Prof. M. Lenzerini for inspiring this example
5. Declarative process modelling
“Open model”
Specify constraints for
permitted behaviour
Every execution that
complies with them is
acceptable
Works best with flexible
processes
The set of DECLARE
templates is extendible
SEITE 5
6. A fragment of declarative
process model
If an abstract is submitted, a new paper
had been written or will be written
After the paper submission, a
confirmation email is sent
After the paper submission, the paper
will be reviewed;
there can be no review without a
preceding submission
A paper can be accepted only after it has
been reviewed
After the rejection, no further
submission follows
Paper cannot be both accepted and
rejected
SEITE 6
Submit abstract Write new paper
Submit paper
Send
confirmation
email
Submit paper Review paper
Review paper Accept paper
Reject paper Submit paper
Accept paper Reject paper
= activation task
Responded existence(Submit abstract, Write new
paper)
Response(Submit paper, Send confirmation email)
Succession(Submit paper, Review paper)
Precedence(Review paper, Accept paper)
Not succession(Reject paper, Submit paper)
Not co-existence(Accept paper, Reject paper)
Template Tasks
7. A fragment of declarative
process model
SEITE 7
Submit abstract
Write new paper Submit paper
Send
confirmation
email
Review paper Accept paper
Reject paper
8. Every DECLARE constraint
can be abstracted as an FSA
SEITE 8
Accepting state
Any task but ‘a’ or ‘b’
Any task but ‘b’Any task
State
FSA: Deterministic Finite State Automaton
Task ‘a’Init
10. Declarative process discovery
SEITE 10
?
Objective: understanding the
constraints that best define
the allowed behaviour of the
process behind the event log
13. Discovering a DECLARE model:
example
Example event log
w a s e s e r r r y e s e
a s e r r y e s e s e
w w w s e r r r r x e
s e r x e
w a s e r r r y e s e
w a s e r r r y e s e
w a s e r r r x e
s e s e s e r r r e x e
a w s e s e r r e x e
w a e s e r r r e y e s e
R.Ex.(s,y)
SEITE 13
Res.(w,y)
Res.(s,e)
Support: 50% 70% 100%
14. Discovering a DECLARE model:
example
Example event log
w a s e s e r r r y e s e
a s e r r y e s e s e
w w w s e r r r r x e
s e r x e
w a s e r r r y e s e
w a s e r r r y e s e
w a s e r r r x e
s e s e s e r r r e x e
a w s e s e r r e x e
w a e s e r r r e y e s e
R.Ex.(s,y)
SEITE 14
Res.(w,y)
Res.(s,e)
Support: 50% 70% 100%
15. Discovering a DECLARE model:
example
Example event log
w a s e s e r r r y e s e
a s e r r y e s e s e
w w w s e r r r r x e
s e r x e
w a s e r r r y e s e
w a s e r r r y e s e
w a s e r r r x e
s e s e s e r r r e x e
a w s e s e r r e x e
w a e s e r r r e y e s e
R.Ex.(s,y)
SEITE 15
Res.(w,y)
Res.(s,e)
Support: 50% 70% 100%
16. A fragment of declarative
process model
SEITE 16
Submit abstract
(a)
Write new paper
(w)
Submit paper
(s)
Send conf. email
(e)
Review paper
(r)
Accept paper
(y)
Reject paper
(x)
?
17. Discovering a DECLARE model:
example
Example event log
w a s e s e r r r y e s e
a s e r r y e s e s e
w w w s e r r r r x e
s e r x e
w a s e r r r y e s e
w a s e r r r y e s e
w a s e r r r x e
s e s e s e r r r e x e
a w s e s e r r e x e
w a e s e r r r e y e s e
R.Ex.(s,y)
SEITE 17
Res.(w,y)
Res.( ,y)
Support: 50% 70% 100%
18. A fragment of declarative
process model
SEITE 18
Submit abstract
(a)
Write new paper
(w)
Submit paper
(s)
Send conf. email
(e)
Review paper
(r)
Accept paper
(y)
Reject paper
(x)
?
19. Vacuity detection
A constraint is vacuously satisfied by a trace
if it is verified yet never “triggered”
E.g., Response(w,y) is vacuously satisfied in
s e r x e
a s e r r y e s e s e
For standard DECLARE templates,
techniques exist that detect the vacuous
satisfaction of constraints
SEITE 19
20. Discovering a DECLARE model:
example with vacuity check (~)
Example event log
w a s e s e r r r y e s e
a s e r r y e s e s e
w w w s e r r r r x e
s e r x e
w a s e r r r y e s e
w a s e r r r y e s e
w a s e r r r x e
s e s e s e r r r e x e
a w s e s e r r e x e
w a e s e r r r e y e s e
R.Ex.(s,y)
SEITE 20
Res.(w,y)
~
~
~
Res.( ,y)
~
~
~
~
~
~
~
~
~
~
Support: 50% 70% 100%
21. Discovering a DECLARE model:
example with vacuity check (~)
Example event log
w a s e s e r r r y e s e
a s e r r y e s e s e
w w w s e r r r r x e
s e r x e
w a s e r r r y e s e
w a s e r r r y e s e
w a s e r r r x e
s e s e s e r r r e x e
a w s e s e r r e x e
w a e s e r r r e y e s e
R.Ex.(s,y)
SEITE 21
Res.(w,y)
~
~
~
Res.( ,y)
~
~
~
~
~
~
~
~
~
~
Support: 50% 70% 100%
22. Res.(w,y)
~
~
~
Discovering a DECLARE model:
example
Example event log
w a s e s e r r r y e s e
a s e r r y e s e s e
w w w s e r r r r x e
s e r x e
w a s e r r r y e s e
w a s e r r r y e s e
w a s e r r r x e
s e s e s e r r r e x e
a w s e s e r r e x e
w a e s e r r r e y e s e
R.Ex.(s,y)
SEITE 22
Res.(s,e)
Support: 50% 70% 100%
23. Summary of the status quo
DECLARE mining techniques return a model
made of those constraints that have a sufficient
fraction of fulfilling traces
Vacuity check already works for standard
DECLARE templates with ad-hoc procedures
DECLARE is extendible
What happens with non-standard DECLARE
templates?
SEITE 23
25. The problem
A general framework for the vacuity detection in
the context of declarative process mining is
missing.
Existing techniques are either syntax-based…
Different formulations of the same constraints lead to
different results
... Or ad-hoc
Not extendible
Result:
Mining non-standard Declare constraints can lead to
loads of vacuously satisfied constraints returned as
if they were interesting discovery results
SEITE 25
26. An example of new template:
“Progression response 3:2”
SEITE 26
Prog.resp3:2(u1,u2,u3, v1,v2)
Prog.resp3:2(Write paper,Submit abstract,Submit paper,
Send notification email,Accept paper)
Example:
27. Discovering a DECLARE model:
example
Example event log
w a s e s e r r r y e s e
a s e r r y e s e s e
w w w s e r r r r x e
s e r x e
w a s e r r r y e s e
w a s e r r r y e s e
w a s e r r r x e
s e s e s e r r r e x e
a w s e s e r r e x e
w a e s e r r r e y e s e
Prog.resp3:2(w,a,s, e,y)
SEITE 27 Support: 90%
28. Discovering a DECLARE model:
example
Example event log
w a s e s e r r r y e s e
a s e r r y e s e s e
w w w s e r r r r x e
s e r x e
w a s e r r r y e s e
w a s e r r r y e s e
w a s e r r r x e
s e s e s e r r r e x e
a w s e s e r r e x e
w a e s e r r r e y e s e
Prog.resp3:2(w,a,s, e,y)
SEITE 28 Support: 90%
29. Discovering a DECLARE model:
example
Example event log
w a s e s e r r r y e s e
a s e r r y e s e s e
w w w s e r r r r x e
s e r x e
w a s e r r r y e s e
w a s e r r r y e s e
w a s e r r r x e
s e s e s e r r r e x e
a w s e s e r r e x e
w a e s e r r r e y e s e
Prog.resp3:2(w,a,s, e,y)
SEITE 29 Support: 90%
30. Discovering a DECLARE model:
example
Example event log
w a s e s e r r r y e s e
a s e r r y e s e s e
w w w s e r r r r x e
s e r x e
w a s e r r r y e s e
w a s e r r r y e s e
w a s e r r r x e
s e s e s e r r r e x e
a w s e s e r r e x e
w a e s e r r r e y e s e
Prog.resp3:2(w,a,s, e,y)
SEITE 30 Support: 90%
31. Discovering a DECLARE model:
example
Example event log
w a s e s e r r r y e s e
a s e r r y e s e s e
w w w s e r r r r x e
s e r x e
w a s e r r r y e s e
w a s e r r r y e s e
w a s e r r r x e
s e s e s e r r r e x e
a w s e s e r r e x e
w a e s e r r r e y e s e
Prog.resp3:2(w,a,s, e,y)
SEITE 31 Support: 90%
32. Discovering a DECLARE model:
example
Example event log
w a s e s e r r r y e s e
a s e r r y e s e s e
w w w s e r r r r x e
s e r x e
w a s e r r r y e s e
w a s e r r r y e s e
w a s e r r r x e
s e s e s e r r r e x e
a w s e s e r r e x e
w a e s e r r r e y e s e
Prog.resp3:2(w,a,s, e,y)
SEITE 32 Support: 90%
33. Discovering a DECLARE model:
example
Example event log
w a s e s e r r r y e s e
a s e r r y e s e s e
w w w s e r r r r x e
s e r x e
w a s e r r r y e s e
w a s e r r r y e s e
w a s e r r r x e
s e s e s e r r r e x e
a w s e s e r r e x e
w a e s e r r r e y e s e
Prog.resp3:2(w,a,s, e,y)
SEITE 33 Support: 90%
34. Discovering a DECLARE model:
example
Example event log
w a s e s e r r r y e s e
a s e r r y e s e s e
w w w s e r r r r x e
s e r x e
w a s e r r r y e s e
w a s e r r r y e s e
w a s e r r r x e
s e s e s e r r r e x e
a w s e s e r r e x e
w a e s e r r r e y e s e
Prog.resp3:2(w,a,s, e,y)
SEITE 34 Support: 90%
35. Discovering a DECLARE model:
example
Example event log
w a s e s e r r r y e s e
a s e r r y e s e s e
w w w s e r r r r x e
s e r x e
w a s e r r r y e s e
w a s e r r r y e s e
w a s e r r r x e
s e s e s e r r r e x e
a w s e s e r r e x e
w a e s e r r r e y e s e
Prog.resp3:2(w,a,s, e,y)
SEITE 35 Support: 90%
36. Discovering a DECLARE model:
example
Example event log
w a s e s e r r r y e s e
a s e r r y e s e s e
w w w s e r r r r x e
s e r x e
w a s e r r r y e s e
w a s e r r r y e s e
w a s e r r r x e
s e s e s e r r r e x e
a w s e s e r r e x e
w a e s e r r r e y e s e
Prog.resp3:2(w,a,s, e,y)
SEITE 36 Support: 90%
37. Discovering a DECLARE model:
example
Example event log
w a s e s e r r r y e s e
a s e r r y e s e s e
w w w s e r r r r x e
s e r x e
w a s e r r r y e s e
w a s e r r r y e s e
w a s e r r r x e
s e s e s e r r r e x e
a w s e s e r r e x e
w a e s e r r r e y e s e
Prog.resp3:2(w,a,s, e,y)
SEITE 37 Support: 90%
38. An example of new template:
“Progression response 3:2”
SEITE 38
Prog.resp3:2(u1,u2,u3, v1,v2)
Prog.resp3:2(Submit paper,Write paper,Submit abstract,
Reject paper,Accept paper)
Example:
It makes no sense. Yet…
39. Discovering a DECLARE model:
example
Example event log
w a s e s e r r r y e s e
a s e r r y e s e s e
w w w s e r r r r x e
s e r x e
w a s e r r r y e s e
w a s e r r r y e s e
w a s e r r r x e
s e s e s e r r r e x e
a w s e s e r r e x e
w a e s e r r r e y e s e
Prog.resp3:2(s,w,a, x,y)
SEITE 39 Support: 100%
“Impossible” activations make for
a support of 100%
40. Discovering a DECLARE model:
example
Example event log
w a s e s e r r r y e s e
a s e r r y e s e s e
w w w s e r r r r x e
s e r x e
w a s e r r r y e s e
w a s e r r r y e s e
w a s e r r r x e
s e s e s e r r r e x e
a w s e s e r r e x e
w a e s e r r r e y e s e
Prog.resp3:2(w,s, , y,e)
SEITE 40 Support: 100%
41. Summary of the status quo
DECLARE mining techniques return a model
made of those constraints that have a sufficient
count of fulfilling traces
Vacuity check already works for standard
DECLARE templates with ad-hoc procedures
DECLARE is extendible
With non-standard DECLARE templates, should
unicorns hold the truth?
SEITE 41
43. The solution: sketch (1)
SEITE 43
For every constraint FSA, activation-aware
automata are built, i.e., states get labelled with:
1. the satisfaction status reached so far
1. temporarily/permanently satisfied/violated: ts, ps, tv, pv
2. the allowed tasks for the future moves
Satisfaction
Allowed tasksSatisfaction
46. The solution: sketch (2)
SEITE 46
Task executions are marked as relevant when
they make the satisfaction status change, or
they make the allowed tasks change
Irrelevant
Relevant
Allowed tasksSatisfaction
Irrelevant
Relevant
47. The solution: sketch (3)
SEITE 47
A trace is an interesting witness when a relevant
task execution is performed
A trace satisfies a constraint when its replay
terminates in an accepting state
We look for interesting traces that satisfy the
constraints
48. Discovering a DECLARE model:
example
Example event log
w a s e s e r r r y e s e
a s e r r y e s e s e
w w w s e r r r r x e
s e r x e
w a s e r r r y e s e
w a s e r r r y e s e
w a s e r r r x e
s e s e s e r r r e x e
a w s e s e r r e x e
w a e s e r r r e y e s e
Prog.resp3:2(w,a,s, e,y)
~
~
~
~
~
SEITE 48 Support: 90%
49. Discovering a DECLARE model:
example
Example event log
w a s e s e r r r y e s e
a s e r r y e s e s e
w w w s e r r r r x e
s e r x e
w a s e r r r y e s e
w a s e r r r y e s e
w a s e r r r x e
s e s e s e r r r e x e
a w s e s e r r e x e
w a e s e r r r e y e s e
Prog.resp3:2(w,a,s, e,y)
~
~
~
~
~
SEITE 49 Support: 90%
50. Discovering a DECLARE model:
example
Example event log
w a s e s e r r r y e s e
a s e r r y e s e s e
w w w s e r r r r x e
s e r x e
w a s e r r r y e s e
w a s e r r r y e s e
w a s e r r r x e
s e s e s e r r r e x e
a w s e s e r r e x e
w a e s e r r r e y e s e
Prog.resp3:2(w,s, , y,e)
~
~
~
~
~
~
~
~
~
~
SEITE 50 Support: 100%
51. Discovering a DECLARE model:
example
Example event log
w a s e s e r r r y e s e
a s e r r y e s e s e
w w w s e r r r r x e
s e r x e
w a s e r r r y e s e
w a s e r r r y e s e
w a s e r r r x e
s e s e s e r r r e x e
a w s e s e r r e x e
w a e s e r r r e y e s e
Prog.resp3:2(w,s, , y,e)
~
~
~
~
~
~
~
~
~
~
SEITE 51 Support: 100%
52. Discovering a DECLARE model:
example with relevance check
Example event log
w a s e s e r r r y e s e
a s e r r y e s e s e
w w w s e r r r r x e
s e r x e
w a s e r r r y e s e
w a s e r r r y e s e
w a s e r r r x e
s e s e s e r r r e x e
a w s e s e r r e x e
w a e s e r r r e y e s e
SEITE 52 Support: 70%
Res.(w,y)
~
~
~
53. Discovering a DECLARE model:
example with relevance check
Example event log
w a s e s e r r r y e s e
a s e r r y e s e s e
w w w s e r r r r x e
s e r x e
w a s e r r r y e s e
w a s e r r r y e s e
w a s e r r r x e
s e s e s e r r r e x e
a w s e s e r r e x e
w a e s e r r r e y e s e
SEITE 53 Support: 70%
Res.(w,y)
~
~
~
54. Discovering a DECLARE model:
example with vacuity check (~)
Example event log
w a s e s e r r r y e s e
a s e r r y e s e s e
w w w s e r r r r x e
s e r x e
w a s e r r r y e s e
w a s e r r r y e s e
w a s e r r r x e
s e s e s e r r r e x e
a w s e s e r r e x e
w a e s e r r r e y e s e
SEITE 54 Support:
Res.( ,y)
~
~
~
~
~
~
~
~
~
~
100%
55. Discovering a DECLARE model:
example with vacuity check (~)
Example event log
w a s e s e r r r y e s e
a s e r r y e s e s e
w w w s e r r r r x e
s e r x e
w a s e r r r y e s e
w a s e r r r y e s e
w a s e r r r x e
s e s e s e r r r e x e
a w s e s e r r e x e
w a e s e r r r e y e s e
SEITE 55 Support:
Res.( ,y)
~
~
~
~
~
~
~
~
~
~
100%
58. Evaluation
SEITE 58
Implemented in Java
Extension of MINERful
Real-world logs
BPIC 2013:
9,442 msec
426 for the automata
9,016 for the checking
59. Conclusion
Contribution:
A generalised approach for the discovery of
interesting declarative constraints
Check based on the relevance of task executions w.r.t.
the semantics of the constraints
Future work:
Differentiation of positive and negative interestingness
Extended declarative mining integrating general
vacuity detection
Data-awareness
SEITE 59
60. Semantical Vacuity Detection in
Declarative Process Mining
Fabrizio Maria Maggi, Marco Montali, Claudio Di Ciccio and Jan Mendling
14th International Conference on Business Process Management
Rio de Janeiro, Brazil
No unicorns were harmed in the making of this paper
61. Semantical Vacuity Detection in
Declarative Process Mining
Fabrizio Maria Maggi, Marco Montali, Claudio Di Ciccio and Jan Mendling
Extra slides
62. A fragment of declarative
process model
SEITE 62
Submit abstract Write new paper
Submit paper
Send
confirmation
email
Submit paper Review paper
Review paper Accept paper
Reject paper Submit paper
Accept paper Reject paper
Responded existence(Submit abstract, Write new
paper)
Response(Submit paper, Send confirmation email)
Succession(Submit paper, Review paper)
Precedence(Review paper, Accept paper)
Not succession(Reject paper, Submit paper)
Not co-existence(Accept paper, Reject paper)
Template Tasks = activation task
63. A fragment of declarative
process model
SEITE 63
Submit abstract
(a)
Write new paper
(w)
Submit paper
(s)
Send conf. email
(e)
Review paper
(r)
Accept paper
(y)
Reject paper
(x)
64. Semantics of Declare:
LTL and LTLf
Linear Temporal Logic (LTL) initially was a
specification language for the execution of
(endless) concurrent programs (Pnueli, 1977)
Syntax (let A be a propositional symbol):
DECLARE was initially based on LTL
SEITE 64
“Until”
“Eventually”“Always”
“Next”
66. Declarative process modelling
“Open model”
Specify constraints for
permitted behaviour
Every execution that
complies with them is
acceptable
Works best with flexible
processes
The set of DECLARE
templates is extendible
SEITE 66
68. Semantics of Declare:
LTL and LTLf
Linear Temporal Logic (LTL) initially was a
specification language for the execution of
(endless) concurrent programs (Pnueli, 1977)
Syntax (let A be a propositional symbol):
Interpretation over infinite traces,
i.e., an infinite sequence of consecutive instants of time
LTLf formulae are meant to be interpreted over
finite traces
“Until”
“Eventually”“Always”
“Next”
SEITE 68
75. Discovering a DECLARE model:
example with relevance check
Example event log
w a s e s e r r r y e s e
a s e r r y e s e s e
w w w s e r r r r x e
s e r x e
w a s e r r r y e s e
w a s e r r r y e s e
w a s e r r r x e
s e s e s e r r r e x e
a w s e s e r r e x e
w a e s e r r r e y e s e
SEITE 75 Support:
Res.(s,e)
100%