Top PDF Declarative Process Mining on the Cloud

Declarative Process Mining on the Cloud

Declarative Process Mining on the Cloud

3.1.4 MINERful Simplification Declarative process discovery algorithms do not take into consideration constraint interac- tion. Hence, discovered process models may contain counter excluding constraints. Addi- tionally, redundant constraints may be the reason for the verbose models. Declare tem- plates are hierarchical; if the child template is satisfied the parent is satisfied as well. For example, responded existence (a, b) can be considered a parent of the response (a, b) con- straint and therefore, it can be inferred that response ⊑ responded existence. Because of this nature the redundancy may occur in discovered declarative process model. The MINERful Simplification algorithm addresses the issues and automatically trims the dis- covered model to exclude repetitions and resolves interaction collisions. The algorithm uses the automata-product monoid concept to assure model consistency and removal of re- dundant constraints [13] [14].
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Aligning Data-Aware Declarative Process Models and Event Logs

Aligning Data-Aware Declarative Process Models and Event Logs

Two ProM plugins are used in the implementation of our solution. ProM is a Java based open source framework that provides a platform for developers to easily develop and/or extend process mining algorithms. We implemented the conformance checking framework, as described in chapter 3, in a ProM plugin called Data Aware Declare Replayer. The plugin takes as input a Data-Aware Declare model and an event log and outputs alignments, for each log trace. For visualization, we implemented another plugin called Data Aware Alignment Result Visualizer. It takes the output of our Data Aware Declare Replayer plugin as input and provides a clean way to visualize alignments for each trace as well as showing the fitness and related statistics. Figure 7 shows a screenshot of the details of trace alignments in the Data Aware Alignment Result Visualizer. The left panel shows the list of traces labelled with the trace name as well as its fitness. Upon clicking one of the traces, the middle part is filled with the details of that trace showing the following information:
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Declarative Sequential Pattern Mining of Care Pathways

Declarative Sequential Pattern Mining of Care Pathways

saying that the pattern support has to be above a given threshold fmin. Sequential pattern mining with ASP has been introduced by Guyet et al. [2] 4 . It encodes the sequential pattern mining task as an ASP program that process sequential data encoded as ASP facts. A sequential pattern mining task is a tuple hS, M, Ci, where S is a set of ASP facts encodings the sequence database, M is a set of ASP rules which yields pattern tuples from database, C is a set of constraints (see [4] for constraint taxonomy). We have S ∪M∪C |= {hp, Tp, Epi}. In our framework, the sequence database is modeled by seq(s,t,e) atoms. Each of these atoms specifies that the event e ∈ I occurred at time t in sequence s . On the other hand, each answer set holds atoms that encode a pattern tuples.
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Data mining in cloud computing

Data mining in cloud computing

Data mining, the extraction of hidden predictive information from large databases, is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. Data mining tools predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions. The automated, prospective analyses offered by data mining move beyond the analyses of past events provided by retrospective tools typical of decision support systems. As data sets have grown in size and complexity, direct hands-on data analysis has increasingly been augmented with indirect, automatic data processing. This has been aided by other discoveries in computer science, such as neural networks, cluster analysis, genetic algorithms (1950s), decision trees (1960s) and support vector machines (1990s). Data mining is the process of applying these methods to data with the intention of uncovering hidden patterns in large data sets.
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SOFTWARE PROCESS MINING

SOFTWARE PROCESS MINING

Nowadays, in the era of social, mobile and cloud computing, different business information systems produce, log and trace regularly terra bytes of data. Process mining deals with transforming this data to a valuable knowledge, which is used for improving the business processes.

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Combining Declarative and Imperative Cloud Application Provisioning based on TOSCA

Combining Declarative and Imperative Cloud Application Provisioning based on TOSCA

In recent years, Cloud Computing gained a lot of attention due to its economical and technical benefits. From an enterprise perspective, Cloud properties such as pay-on-demand pricing, scalability, and self-service enable outsourcing the enterprise’s IT. This helps to achieve flexible, automated, and cheap IT op- eration and management [1]. On the other side, Cloud providers have to automate their internal Cloud management processes to achieve these properties for their Cloud offerings [2]. Especially the rapid provisioning of applications is of vital importance to enable self-service and pay-on-demand pricing. Therefore, one of the most important issues from a Cloud provider’s perspective is to fully automate these provisioning processes. The complexity of provisioning mainly depends on the application’s structure and its components. Cloud applications typically consist of various types of components and employ several heterogeneous Cloud services. This complicates the provisioning because these components typically provide propri- etary management APIs, are based on custom data formats, and use different technologies [2]. For example, IaaS offerings, such as Amazon EC2 1 , are typically managed by using Web Service APIs whereas the installation of Web Servers, such as Apache Tomcat on an Ubuntu Linux operating system, is often done using script-centric technologies such as Chef 2 or Juju 3 . Thus, to fully automate the provisioning of such composite Cloud applications, different management technologies and APIs have to be integrated into one overall provisioning process. This is
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Aligning Event Logs and Declarative Process Models for Conformance Checking

Aligning Event Logs and Declarative Process Models for Conformance Checking

Abstract. Process mining can be seen as the “missing link” between data min- ing and business process management. Although nowadays, in the context of process mining, process discovery attracts the lion’s share of attention, confor- mance checking is at least as important. Conformance checking techniques ver- ify whether the observed behavior recorded in an event log matches a modeled behavior. This type of analysis is crucial, because often real process executions deviate from the predefined process models. Although there exist solid confor- mance checking techniques for procedural models, little work has been done to adequately support conformance checking for declarative models. Typically, traces are classified as fitting or non-fitting without providing any detailed diag- nostics. This paper aligns event logs and declarative models, i.e., events in the log are related to activities in the model if possible. The alignment provides then so- phisticated diagnostics that pinpoint where deviations occur and how severe they are. The approach has been implemented in ProM and has been evaluated using both synthetic logs and real-life logs from Dutch municipalities.
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Process Mining Manifesto

Process Mining Manifesto

model is compared with an event log of the same process. Conformance checking can be used to check if reality, as recorded in the log, conforms to the model and vice versa. Note that different types of models can be considered: conformance checking can be applied to procedural models, organizational models, declarative process models, business rules/policies, laws, etc. The third type of process mining is enhancement. Here, the idea is to extend or improve an existing process model using information about the actual process recorded in some event log. Whereas conformance checking measures the alignment between model and reality, this third type of process mining aims at changing or extending the a-priori model. For instance, by using timestamps in the event log one can extend the model to show bottlenecks, service levels, throughput times, and frequencies.
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Data-aware Synthetic Log Generation for Declarative Process Models

Data-aware Synthetic Log Generation for Declarative Process Models

2 Data-aware Synthetic Log Generation for Declarative Process Models Abstract: In Business Process Management, process mining is a class of techniques for learning pro- cess structure from an execution log. This structure is represented as a process model: either procedural or declarative. Examples of declarative languages are Declare, DPIL and DCR Graphs. In order to test and improve process mining algorithms a lot of logs with different parameters are required, and it is not always possible to get enough real logs. And this is where artificial logs are useful. There exist techniques for log generation from DPIL and declare-based models. But there are no tools for generating logs from MP-Declare – multi- perspective version of Declare with data support. This thesis introduces an approach to log generation from MP-Declare models using two different model checkers: Alloy and NuSMV. In order to improve performance, we applied optimization to baseline approaches available in the literature. All of the discussed techniques are implemented and tested using existing conformance checking tools and our tests. To evaluate performance of our genera- tors and compare them with existing ones, we measured time required for generating log and how it changes with different parameters and models. We also designed several metrics for computing log variability, and applied them to reviewed generators.
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Configurable Services in the Cloud: Supporting Variability While Enabling Cross-Organizational Process Mining

Configurable Services in the Cloud: Supporting Variability While Enabling Cross-Organizational Process Mining

4.2 Example In [17] we analyzed four of the most frequently executed processes in munic- ipalities: (a) acknowledging an unborn child, (b) registering a newborn child, (c) marriage, and (d) issuing a death certificate. Any municipality has these processes, however, as we found out, these processes are implemented and ex- ecuted differently among municipalities. In [17] we compared the processes of four municipalities and the reference model provided by the NVVB (Nederlandse Vereniging Voor Burgerzaken). For example, Figure 6 shows four variants of the process related to “acknowledging an unborn child”. Each of the four municipal- ities is using a specific variant of the processes. Moreover, the NVVB reference model (not shown in Figure 6) is yet another variant of the same process. Based on a detailed analysis of the differences we derived a configurable process model, i.e., a model that captures all variants observed. By setting the configuration parameters, one can reconstruct each of the original process models (and many more). The study reported in [17] revealed that: (a) it is possible to construct (or even generate) configurable process models for the core processes in munic- ipalities, (b) municipalities use similar, but at the same time really different, processes, and (c) the comparison of the same process in multiple municipalities provides interesting insights and triggers valuable discussions.
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EM-BrA2CE v0.1: A vocabulary and execution model for declarative business process modeling

EM-BrA2CE v0.1: A vocabulary and execution model for declarative business process modeling

In this section a formal execution semantics for the EM-BrA 2 CE framework is provided in terms of timed Colored Petri Nets (CP-Net). There are several reasons for choosing CP-Nets. First of all, CP-Nets have a formal semantics (Jensen, 1993, 1996). Furthermore CP-Nets represent an expressive, high-level modeling language that portrays more modeling convenience compared to, for instance, classical Petri nets. Although each CP-net can be translated into a classical Petri net and vice versa, this does not guarantee the suitability of Petri Nets for modeling in practice (Jensen, 1993). In particular, it is difficult to model data manipulations with classical Petri nets, not allowing for token colors. Another reason for using CP-Nets is that CP-Net models can be simulated, making CP-Nets suitable for rapid prototyping process models and for generating artificial data sets of event logs that can later be used to evaluate the performance of process mining algorithms (Goedertier et al., 2007b). Additionally, CP-Nets allow for formal state space analysis that would, in theory, allow for directly analyzing the state space of individual declarative business process models. However, the inclusion of fact-oriented case data and event history into the state space of process models can be expected to result in too large a state space for analyzing realistic models. Consequently, reduction techniques would have to be put in place to reduce the state space into a state space of interest.
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iPOJO flow:a declarative service workflow architecture for ubiquitous cloud applications

iPOJO flow:a declarative service workflow architecture for ubiquitous cloud applications

The current OSGi specification runs short of fully supporting complicated composition topologies like workflow-based orchestrations. Several research efforts were made to overcome such limitation by tapping WS-BPEL technology for complicated orchestration capacity. For instance, an OWL-S/OSGi framework was proposed to support BPEL-style services compositions on top of OSGi platforms(Díaz Redondo et al. 2007). According to the proposal, service matchmaking is made possible based on semantic descriptions of OSGi services. OSGi services can be packaged and offered to the outside world as Web Services, so that they can take part in a BPEL workflow(Anke and Sell 2007). These OSGi-backed Web Services can be combined into a business process described in BPEL, and its materialized workflow is executed by a BPEL engine. On the contrary, a BPEL service can be brought to the OSGi domain to become part of a workflow(Á lamo et al. 2010). Another noteworthy is a BPEL-based service composition framework that is made capable of cross-breeding SOAP, RESTful, and OSGi services by employing adapter patterns(Liu et al. 2013). For adapting OSGi services into BPEL equivalents, the research extended WSDL description for OSGi services including service types, service names, and filters.
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Discovering Data-Aware Declarative Process Models from Event Logs

Discovering Data-Aware Declarative Process Models from Event Logs

The Process Mining Manifesto [9] argues that one of the open challenges in process mining is to find a suitable representational bias (language) to visualize the resulting models. The suitability of a language largely depends on the level of standardization and the environment of the process. Standardized processes in stable environments (e.g., a process for handling insurance claims) are char- acterized by low complexity of collaboration, coordination and decision making. In addition, they are highly predictable, meaning that it is feasible to determine the path that the process will follow. On the other hand, processes in dynamic environments are more complex and less predictable. They comprise a very large number of possible paths as process participants have considerable freedom in determining the next steps in the process (e.g., a doctor in a healthcare process). As discussed in [24, 20, 23], procedural languages, such as BPMN, EPCs and Petri nets, are suitable for describing standardized processes in stable environ- ments. Due to their predictability and low complexity, these processes can be described under a “closed world” assumption, meaning that it is feasible to ex- plicitly represent all the allowed behavior of the process. In contrast, the use of procedural languages for describing processes in dynamic environments leads to complex and incomprehensible models. In this context, declarative process mod- eling languages are more appropriate [23]. Unlike their procedural counterparts, declarative models describe a process under an “open world” assumption, such that everything is allowed unless it is explicitly forbidden. Accordingly, a declar- ative model focuses on capturing commitments and prohibitions that describe what must or must not occur in a given state of the process.
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Efficient Discovery of Understandable Declarative Process Models from Event Logs

Efficient Discovery of Understandable Declarative Process Models from Event Logs

– Second, of the millions of potential constraints, many may be trivially true. For example, the response constraint in Fig. 2 holds for any event log that does not contain events relating to activity a. Moreover, one constraint may dominate another constraint. If the stronger constraint holds (e.g., (a → ♦b)), then automatically the weaker constraint (e.g., ♦a → ♦b) also holds. Showing all constraints that hold typically results in unreadable models. This paper addresses these two problems using a two-phase approach. In the first phase, we generate the list of candidate constraints by using an Apriori al- gorithm. This algorithm is inspired by the seminal Apriori algorithm developed by Agrawal and Srikant for mining association rules [7]. The Apriori algorithm uses the monotonicity property that all subsets of a frequent item-set are also frequent. In the context of this paper, this means that sets of activities can only be frequent if all of their subsets are frequent. This observation can be used to dramatically reduce the number of interesting candidate constraints. In the sec- ond phase, we further prune the list of candidate constraints by considering only the ones that are relevant (based on the event log) according to (the combination of) simple metrics, such as Confidence and Support, and more sophisticated met- rics, such as Interest Factor (IF) and Conditional-Probability Increment Ratio (CPIR), as explained in Section 4. Moreover, discovered constraints with high CPIR values are emphasized like highways on a roadmap whereas constraints with low CPIR values are greyed out. This further improves the readability of discovered Declare models.
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MiningZinc: A declarative framework for constraint-based mining

MiningZinc: A declarative framework for constraint-based mining

The declarative constraint programming approach contrasts with the typical procedural approach to data mining. The latter has focussed on handling large and complex datasets that arise in particular applications, often focussing on special-purpose algorithms to specific problems. This typically yields complex code that is not only hard to develop but also to reuse in other applications. Data mining has devoted less attention than constraint programming to the issue of general and generic solution strategies. Today, there is only little sup- port for formalizing a mining task and capturing a problem specification in a declarative way. Developing and implementing the algorithms is labor inten- sive with only limited re-use of software. The typical iterative nature of the knowledge-discovery cycle [2] further complicates this process, as the problem specification may change between iterations, which may in turn require changes to the algorithms.
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Data mining: From procedural to declarative approaches

Data mining: From procedural to declarative approaches

Related to this is the problem of interpretation. It is well known that statistical methods are often used incorrectly, leading to incorrect conclusions; the field of machine learning has also suffered from this. 12, 13) In data mining, too, using the wrong method and interpreting its results may lead to false conclusions. The solution to both problems is in making data analysis more declar- ative. It should be possible for the user to describe the data analysis problem, rather than having to describe a method for solving it. Compare this to how SQL is used in database technology: SQL made it possible for users to set up and query databases in a much simpler manner than before. The user can ask complex questions without having to know any details about the complex re- trieval procedures that are needed to answer that query. This has not only made it much easier to use databases, it has also led to better efficiency (the system chooses the execution strategy that it thinks is most efficient, rather than leaving this to the user) and made the process less error-prone (manually programming complex retrieval procedures is bound to lead to bugs). The ultimate goal of research on declarative data mining is to similarly change the way users are analyzing data.
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Probabilistic Declarative Process Mining

Probabilistic Declarative Process Mining

Abstract. The management of business processes is receiving much at- tention, since it can support significant efficiency improvements in orga- nizations. One of the most interesting problems is the representation of process models in a language that allows to perform reasoning on it. Various knowledge-based languages have been lately developed for such a task and showed to have a high potential due to the advantages of these languages with respect to traditional graph-based notations. In this work we present an approach for the automatic discovery of knolwedge-based process models expressed by means of a probabilistic logic, starting from a set of process execution traces. The approach first uses the DPML algorithm [16] to extract a set of integrity constraints from a collection of traces. Then, the learned constraints are translated into Markov Logic formulas and the weights of each formula are tuned using the Alchemy system. The resulting theory allows to perform proba- bilistic classification of traces. We tested the proposed approach on a real database of university students’ careers. The experiments show that the combination of DPML and Alchemy achieves better results than DPML alone.
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Declarative Automated Cloud Resource Orchestration

Declarative Automated Cloud Resource Orchestration

• Distributed optimizations. To scale the above configu- ration process and provide autonomy to smaller groups of local administrators such as federated cloud [6] infrastruc- tures, we extend the earlier formulation to consider a dis- tributed optimizations-based approach. This approach al- lows different cloud operators to configure a smaller set of resources while coordinating amongst themselves to achieve a global objective. COPE utilizes a distributed query engine integrated with constraint solving capabilities for coordinating distributed optimizations. We demonstrate its feasibility via a case study involving dynamic load- balancing across data centers. We conclude with a discus- sion of open issues and challenges, and speculate how dis- tribution optimizations may apply to a number of emerg- ing cloud scenarios.
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business process mining

business process mining

There are several studies in the literature that address the problem of preserving the embedded business knowledge in software modernisation processes in traditional (non-process-aware) information systems. Some are based on static analysis of source code like Zou and Hung (2006), who developed a framework to recover workflows from LISs. This framework statically analyses the source code and applies a set of heuristic rules to discover business knowledge from source code. Similarly, Pe´rez-Castillo et al. (2009) propose MARBLE, an architecture-driven modernisation (ADM) framework that uses the knowledge discovery metamodel (KDM) standard to obtain business processes by statically analysing legacy source code. In addition to source code, other works consider additional legacy software artefacts. For example, Paradauskas and Laurikaitis (2006) present a framework to recover business knowledge through the inspection of the data stored in databases. Ghose et al. (2007), in turn, propose a set of text-based queries in source code and documentation for extracting business knowledge. Motahari Nezhad et al. (2008) propose Process Spaceship, a static analysis-based technique to discover process views from process related data sources (e.g. web service logs). This approach addresses the event correlation challenge using correlation conditions defined as binary predicates over the event contents by business experts. Motahari et al. (2007) propose a similar approach that uses web service logs to deal with the event correlation challenge. However, this approach uses graph theory techniques instead of information provided by business experts.
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The use of Process Mining in Business Process Auditing

The use of Process Mining in Business Process Auditing

1. The completeness of the log. Before starting the audit, the event log of the IT systems that support the business process must be exported and loaded in the process mining tool for analysis. We found during our research, that the completeness of the event log is dependent on the detailed specification of the database fields a system database administrator has to export. This in turn is dependent on the accuracy of the mental representation that audit- and business process expert have of the normative process model and the workflow that is programmed in the IT systems. As a rule, this accuracy improves by engaging in active meetings with peer experts while discussing the mental representation. For the audit process approach, a possible solution to this limiting factor can be found in performing multiple iterations of steps 3 and 4 (Process Discovery and Filtering against the normative process model). As more information that is needed to verify audit criteria is identified, a better a more complete event log can be exported. This may lead to a reduction in the number of limitations related to incomplete logging.
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