Web service composition has been one of the most researched topics of the past decade. Novel methods of web service composition are being proposed in the literature include Semantics-based composition, WSDLbased composition. Although these methods provide promising results for composition, search and discovery of web service based on QoS parameter of network and semantics or ontology associated with WSDL, they do not address composition based on usage of web service. Web Service usage logs capture time series data of web service invocation by business objects, which innately captures patterns or workflows associated with business operations. Web service composition based on such patterns and workflows can greatly streamline the business operations. In this research work, we try to explore and implement methods of mining web service usage logs. Main objectives include Identifying usage association of services. Linking one service invocation with other, Evaluation of the causal relationship between associations of services
Event logs recorded during the execution of business processes constitute a valuable source of information. Applying process mining techniques to them, event logs may reveal the actual process execution and enable reasoning on quantitative or qualitative process properties. However, event logs often contain sensitive information that could be related to individual process stakeholders through background information and cross-correlation. We therefore argue that, when publishing event logs, the risk of such re-identification attacks must be considered. In this paper, we show how to quantify the…
Workflow technology is widely used to facilitate the business process in enterprise information systems (EIS), and it has the potential to reduce design time, enhance product quality and decrease product cost. However, significant limitations still exist: as an important task in the context of workflow, many present resource allocation operations are still performed manually, which are time-consuming. This paper presents a data mining approach to address the resource allocation problem (RAP) and improve the productivity of workflow resource management. Specifically, an Apriori-like algorithm i…
The applicability of process mining techniques hinges on the availability of event logs capturing the execution of a business process. In some use cases, particularly those involving customer-facing processes, these event logs may contain private information. Data protection regulations restrict the use of such event logs for analysis purposes. One way of circumventing these restrictions is to anonymize the event log to the extent that no individual can be singled out using the anonymized log. This paper addresses the problem of anonymizing an event log in order to guarantee that, upon disclos…
The applicability of process mining techniques hinges on the availability of event logs capturing the execution of a business process. In some use cases, particularly those involving customer-facing processes, these event logs may contain private information. Data protection regulations restrict the use of such event logs for analysis purposes. One way of circumventing these restrictions is to anonymize the event log to the extent that no individual can be singled out using the anonymized log. This article addresses the problem of anonymizing an event log in order to guarantee that, upon relea…
One of the most valuable assets of an organization is its organizational data. The analysis and mining of this potential hidden treasure can lead to much added-value for the organization. Process mining is an emerging area that can be useful in helping organizations understand the status quo, check for compliance and plan for improving their processes. The aim of process mining is to extract knowledge from event logs of today's organizational information systems. Process mining includes three main types: discovering process models from event logs, conformance checking and organizational mining…
Intention-oriented process mining is based on the belief that the fundamental nature of processes is mostly intentional (unlike activity-oriented process) and aims at discovering strategy and intentional process models from event-logs recorded during the process enactment. In this paper, we present an application of intention-oriented process mining for the domain of incident management of an Information Technology Infrastructure Library (ITIL) process. We apply the Map Miner Method (MMM) on a large real-world dataset for discovering hidden and unobservable user behavior, strategies and intent…
In this work, we propose an approach for collecting completion usage logs from the users in an IDE and using them to train a machine learning based model for ranking completion candidates. We developed a set of features that describe completion candidates and their context, and deployed their anonymized collection in the Early Access Program of IntelliJ-based IDEs. We used the logs to collect a dataset of code completions from users, and employed it to train a ranking CatBoost model. Then, we evaluated it in two settings: on a held-out set of the collected completions and in a separate A/B tes…
When programmers retrieve a code method and want to reuse it, they need to understand the usage patterns of the retrieved method. However, it is difficult to obtain usage information of the retrieved method since this method may only have a brief comment and few available usage examples. In this paper, we propose an approach, called LUPIN (cLone-based Usage Pattern mIniNg), to mine the usage patterns of these methods, which do not widely appeared in the code repository. The key idea of LUPIN is that the cloned code of the target method may have a similar usage pattern, and we can collect more …
Recently, information systems like ERP, CRM and WFM record different business events or activities in a log named as event log. Process mining aims at extracting information from event logs to capture business process as it is being executed. Process mining is an important learning task based on captured processes. In order to be competent organizations in the business world; they have to adjust their business process along with the changing environment. Sometimes a change in the business process implies a change into the whole system. Process mining allows for the automated discovery of proce…
A Web API (WAPI) is a type of API whose interaction with its consumers is done through the Internet. While being accessed through the Internet can be challenging, mostly when WAPIs evolve, it gives providers the possibility to monitor their usage, and understand and analyze consumers' behavior. Currently, WAPI usage is mostly logged for traffic monitoring and troubleshooting. Even though they contain invaluable information regarding consumers' behavior} they are not sufficiently used by providers. In this paper, we first consider two phases of the application development lifecycle, and based o…
Modern information systems are able to collect event data in the form of event logs. Process mining techniques allow to discover a model from event data, to check the conformance of an event log against a reference model, and to perform further process-centric analyses. In this paper, we consider uncertain event logs, where data is recorded together with explicit uncertainty information. We describe a technique to discover a directly-follows graph from such event data which retains information about the uncertainty in the process. We then present experimental results of performing inductive mi…
Process mining allows analysts to exploit logs of historical executions of business processes to extract insights regarding the actual performance of these processes. One of the most widely studied process mining operations is automated process discovery. An automated process discovery method takes as input an event log, and produces as output a business process model that captures the control-flow relations between tasks that are observed in or implied by the event log. Various automated process discovery methods have been proposed in the past two decades, striking different tradeoffs between…
We propose a framework to mine API usage scenarios from Stack Overflow. Each task consists of a code example, the task description, and the reactions of developers towards the code example. First, we present an algorithm to automatically link a code example in a forum post to an API mentioned in the textual contents of the forum post. Second, we generate a natural language description of the task by summarizing the discussions around the code example. Third, we automatically associate developers reactions (i.e., positive and negative opinions) towards the code example to offer information abou…
Process mining involves discovering, monitoring, and improving real processes by extracting knowledge from event logs in information systems. Process mining has become an important topic in recent years, as evidenced by a growing number of case studies and commercial tools. Current studies in this area assume that event records are created separately from a conceptual model (CM). Techniques are then used to discover missing processes and conformance with the CM, as well as for checks and enhancements. By contrast, in this paper we focus on modeling events as part of a tight multilevel CM that …
Indraprastha Institute of Information Technology, Delhi (IIIT-D)（德里英德拉普拉斯塔信息技术学院（IIIT-D））；Software Analytics Research Lab (SARL)（软件分析研究实验室（SARL））
Issue Tracking Systems (ITS) such as Bugzilla can be viewed as Process Aware Information Systems (PAIS) generating event-logs during the life-cycle of a bug report. Process Mining consists of mining event logs generated from PAIS for process model discovery, conformance and enhancement. We apply process map discovery techniques to mine event trace data generated from ITS of open source Firefox browser project to generate and study process models. Bug life-cycle consists of diversity and variance. Therefore, the process models generated from the event-logs are spaghetti-like with large number o…
Logs are widely used to record runtime information of software systems, such as the timestamp and the importance of an event, the unique ID of the source of the log, and a part of the state of a task's execution. The rich information of logs enables system developers (and operators) to monitor the runtime behaviors of their systems and further track down system problems and perform analysis on log data in production settings. However, the prior research on utilizing logs is scattered and that limits the ability of new researchers in this field to quickly get to the speed and hampers currently …
GUI-based models extracted from Android app execution traces, events, or source code can be extremely useful for challenging tasks such as the generation of scenarios or test cases. However, extracting effective models can be an expensive process. Moreover, existing approaches for automatically deriving GUI-based models are not able to generate scenarios that include events which were not observed in execution (nor event) traces. In this paper, we address these and other major challenges in our novel hybrid approach, coined as MonkeyLab. Our approach is based on the Record-Mine-Generate-Valida…
Software developers study and reuse existing source code to understand how to properly use application programming interfaces (APIs). However, manually finding sufficient and adequate code examples for a given API is a difficult and a time-consuming activity. Existing approaches to find or generate examples assume availability of a reasonable set of client code that uses the API. This assumption does not hold for newly released API libraries, non-widely used APIs, nor private ones. In this work we reuse the important information that is naturally present in test code to circumvent the lack of …
Despite being one of the largest and most popular projects, the official Android framework has only provided test cases for less than 30% of its APIs. Such a poor test case coverage rate has led to many compatibility issues that can cause apps to crash at runtime on specific Android devices, resulting in poor user experiences for both apps and the Android ecosystem. To mitigate this impact, various approaches have been proposed to automatically detect such compatibility issues. Unfortunately, these approaches have only focused on detecting signature-induced compatibility issues (i.e., a certai…
Process mining is a new emerging research trend over the last decade which focuses on analyzing the processes using event log and data. The raising integration of information systems for the operation of business processes provides the basis for innovative data analysis approaches. Process mining has the strong relationship between with data mining so that it enables the bond between business intelligence approach and business process management. It focuses on end to end processes and is possible because of the growing availability of event data and new process discovery and conformance checki…
When smartphones, applications (a.k.a, apps), and app stores have been widely adopted by the billions, an interesting debate emerges: whether and to what extent do device models influence the behaviors of their users? The answer to this question is critical to almost every stakeholder in the smartphone app ecosystem, including app store operators, developers, end-users, and network providers. To approach this question, we collect a longitudinal data set of app usage through a leading Android app store in China, called Wandoujia. The data set covers the detailed behavioral profiles of 0.7 milli…
Enterprise information systems allow companies to maintain detailed records of their business process executions. These records can be extracted in the form of event logs, which capture the execution of activities across multiple instances of a business process. Event logs may be used to analyze business processes at a fine level of detail using process mining techniques. Among other things, process mining techniques allow us to discover a process model from an event log -- an operation known as automated process discovery. Despite a rich body of research in the field, existing automated proce…
University of Tartu（塔尔图大学）；Universidad de los Andes（洛斯安第斯大学）；Apromore（Apromore公司）
Business process simulation is a well-known approach to estimate the impact of changes to a process with respect to time and cost measures -- a practice known as what-if process analysis. The usefulness of such estimations hinges on the accuracy of the underlying simulation model. Data-Driven Simulation (DDS) methods leverage process mining techniques to learn process simulation models from event logs. Empirical studies have shown that, while DDS models adequately capture the observed sequences of activities and their frequencies, they fail to accurately capture the temporal dynamics of real-l…
