Single Process Instance Research Articles

Remaining time prediction for business processes with concurrency based on log representation

Remaining time prediction of business processes plays an important role in resource scheduling and plan making. The structural features of single process instance and the concurrent running of multiple process instances are the main factors that affect the accuracy of the remaining time prediction. Existing prediction methods does not take full advantage of these two aspects into consideration. To address this issue, a new prediction method based on trace representation is proposed. More specifically, we first associate the prefix set generated by the event log to different states of the transition system, and encode the structural features of the prefixes in the state. Then, an annotation containing the feature representation for the prefix and the corresponding remaining time are added to each state to obtain an extended transition system. Next, states in the extended transition system are partitioned by the different lengths of the states, which considers concurrency among multiple process instances. Finally, the long short-term memory (LSTM) deep recurrent neural networks are applied to each partition for predicting the remaining time of new running instances. By extensive experimental evaluation using synthetic event logs and reallife event logs, we show that the proposed method outperforms existing baseline methods.

Dynamic Provisioning of Service Composition in a Multi-Tenant SaaS Environment

Multi-tenant service composition has become a common delivery model for business processes in cloud computing. To dynamically support the workload tenant variation, elasticity holds the promise of ensuring the quality of service (QoS) of the business process by providing the involved service instances at a low cost. However, integrating both of multi-tenancy and elasticity during service composition is a key problem for serving multiple tenants from a single process instance. Nowadays, existing approaches in the field of cloud service composition, although numerous, still fall short since they cannot adequately address issues related to supporting the scalability of the composed service and adapting it to the workload fluctuation. In this paper, we propose a holistic approach which makes the dynamic multi-tenant services matching and manages their elasticity in distributed business processes. This approach is based on a generic service pattern that integrates multi-tenancy property and handles elasticity at the process and service levels. Furthermore, we present elastic composition algorithms to compose multi-tenant cloud services and perform their elasticity through the proposed service pattern. The evaluation of our approach, compared to the baseline approach, proves that the latency taken to provide an elastic multi-tenant service composition and detect its SLA (Service Level Agreements) violation are reasonably short. We also show that the CPU overhead of using our approach is negligible. Furthermore, experimental results demonstrate the merits of our approach in terms of minimizing the memory consumption through the deployed service instances.

Time prediction on multi-perspective declarative business processes

Process-aware information systems (PAISs) are increasingly used to provide flexible support for business processes. The support given through a PAIS is greatly enhanced when it is able to provide accurate time predictions which is typically a very challenging task. Predictions should be (1) multi-dimensional and (2) not based on a single process instance. Furthermore, the prediction system should be able to (3) adapt to changing circumstances and (4) deal with multi-perspective declarative languages (e.g., models which consider time, resource, data and control flow perspectives). In this work, a novel approach for generating time predictions considering the aforementioned characteristics is proposed. For this, first, a multi-perspective constraint-based language is used to model the scenario. Thereafter, an optimized enactment plan (representing a potential execution alternative) is generated from such a model considering the current execution state of the process instances. Finally, predictions are performed by evaluating a desired function over this enactment plan. To evaluate the applicability of our approach in practical settings we apply it to a real process scenario. Despite the high complexity of the considered problems, results indicate that our approach produces a satisfactory number of good predictions in a reasonable time.

Predictive monitoring of temporally-aggregated performance indicators of business processes against low-level streaming events

Monitoring the performances of a business process is a key issue in many organizations, especially when the process must comply with predefined performance constraints. In such a case, empowering the monitoring system with prediction capabilities would allow us to know in advance a constraint violation, and possibly trigger corrective measures to eventually prevent the violation. Despite the problem of making run-time predictions for a process, based on pre-mortem log data, is an active research topic in Process Mining, current predictive monitoring approaches in this field only support predictions at the level of a single process instance, whereas process performance constraints are often defined in an aggregated form, according to predefined time windows. Moreover, most of these approaches cannot work well on the traces of a lowly-structured business process when these traces do not refer to well-defined process tasks/activities. For such a challenging setting, we define an approach to the problem of predicting whether the process instances of a given (unfinished) time window will violate an aggregate performance requirement. The approach mainly rely on inducing and integrating two complementary predictive models: (1) a clustering-based predictor for estimating the outcome of each ongoing process instance, (2) a time-series predictor for estimating the performance outcome of “future” process instances that will fall in the window after the moment when the prediction is being made (i.e. instances, not started yet, that will start by the end of the window). Both models are expected to benefit from the availability of aggregate context data regarding the environment that surrounds the process. This discovery approach is conceived as the core of an advanced performance monitoring system, for which an event-based conceptual architecture is here proposed. Tests on real-life event data confirmed the validity of our approach, in terms of accuracy, robustness, scalability, and usability.

Supporting Customizable Business Process Models Using Graph Transformation Rules

Business Process customization is an active research area in the process management field, dealing with variations/commonalities among processes of a given process family and runtime adaptations of single process instances. Many theoretical approaches have been suggested in the last years; however, practical implementations are rare and limited in their functionality. In this article, a new approach is proposed for capturing customizable process models based on well-known graph transformation techniques and with focus on practical aspects like definition of variation points, linking and propagation of changes, visual highlighting of differences in process variants, and dynamically selecting a specific variant at runtime. The suggested concepts are discussed within case studies, comprising different graph transformation systems for generating process variants supporting (a) variability by restriction, (b) variability by restriction and by extension, and (c) runtime adaptations due to the executing actor. The overall approach is being implemented in the FireStart BPM suite.

A Dynamic System Architecture for Safety Related Systems

Safety related applications usually demand the provision of redundant resources within the system and some method of reconfiguration when a failure is detected. One problem with such an approach is that it has proved to be very difficult to design, implement and test adequately this reconfigurability. A dynamic system architecture is described which obviates some of these difficulties. This architecture takes advantage of the fact that the processing associated with any set of inputs and at any instant of time is of finite duration. By arranging for sufficient parallel redundancy to be available so that the system is not compromised by a single process instance failure, system error recovery becomes almost trivial-There is no need to recover the single instance failure (because of the available redundancy) and future processing will be initiated by normal process initiation. Little error-recovery specific procedure is necessary other than producing an effective fail-stop processor. The efficient implementation of such a system depends crucially on a number of issues. These include a novel, fully distributed scheduling procedure and the topology and functionality of the underlying communication system. The implications of such an architecture for overall system safety include the effects and benefits of software diversity and the possibility of producing systems which are to some extent proof against their own design errors.