Abderrahmane Maaradji

Social-Based Web Services Discovery and Composition for Step-by-Step Mashup Completion

In this paper, we describe our work in progress on Web services recommendation for services composition in a Mashup environment, by proposing a new approach to assist end-users based social interactions capture and analysis. This approach uses an implicit social graph inferred from the common composition interests of users. We describe the transformation of users-services interactions into a social graph and a possible means to leverage that graph to derive service recommendation. As this work is in progress, this proposal was implemented within a platform called SoCo where preliminary experiments show interesting results.

Fast and Accurate Business Process Drift Detection

Business processes are prone to continuous and unexpected changes. Process workers may start executing a process differently in order to adjust to changes in workload, season, guidelines or regulations for example. Early detection of business process changes based on their event logs --- also known as business process drift detection --- enables analysts to identify and act upon changes that may otherwise affect process performance. Previous methods for business process drift detection are based on an exploration of a potentially large feature space and in some cases they require users to manually identify the specific features that characterize the drift. Depending on the explored feature set, these methods may miss certain types of changes. This paper proposes a fully automated and statistically grounded method for detecting process drift. The core idea is to perform statistical tests over the distributions of runs observed in two consecutive time windows. By adaptively sizing the window, the method strikes a trade-off between classification accuracy and drift detection delay. A validation on synthetic and real-life logs shows that the method accurately detects typical change patterns and scales up to the extent that it works for online drift detection.

Social Web Mashups Full Completion via Frequent Sequence Mining

In this paper we address the problem of Web Mashups full completion which consists of predicting the most suitable set of (combined) services that successfully meet the goals of an end-user Mashup, given the current service (or composition of services) initially supplied. We model full completion as a frequent sequence mining problem and we show how existing algorithms can be applied in this context. To overcome some limitations of the frequent sequence mining algorithms, e.g., efficiency and recommendation granularity, we propose FESMA, a new and efficient algorithm for computing frequent sequences of services and recommending completions. FESMA also integrates a social dimension, extracted from the transformation of user-service interactions into user-user interactions, building an implicit graph that helps to better predict completions of services in a fashion tailored to individual users. Evaluations show that FESMA is more efficient outperforming the existing algorithms even with the consideration of the social dimension. Our proposal has been implemented in a prototype, SoCo, developed at Bell Labs.

Developing Compelling Social-Enabled Applications with Context-Based Social Interaction Analysis

We present in this paper a new approach for constructing implicit social networks from electronic sources, like emails, SMS and phone calls. The main novelty is the use of Social Interaction Analysis to assist end-users in their communication needs. We discuss how a social proximity can be calculated between two persons in a social network and show how a weighted, directed network is constructed based on interactions between people. After a description of the architecture of the framework, we show how a contextual, weighted social network can help in automatically finding the contact with highest probability to know the whereabouts of a person who is currently not reachable. The implemented social helper application uses a specific contextual view of the social network, exploiting only interactions that occurred in the last 48 hours.

Detecting drift from event streams of unpredictable business processes

Existing business process drift detection methods do not work with event streams. As such, they are designed to detect inter-trace drifts only, i.e. drifts that occur between complete process executions (traces), as recorded in event logs. However, process drift may also occur during the execution of a process, and may impact ongoing executions. Existing methods either do not detect such intra-trace drifts, or detect them with a long delay. Moreover, they do not perform well with unpredictable processes, i.e. processes whose logs exhibit a high number of distinct executions to the total number of executions. We address these two issues by proposing a fully automated and scalable method for online detection of process drift from event streams. We perform statistical tests over distributions of behavioral relations between events, as observed in two adjacent windows of adaptive size, sliding along with the stream. An extensive evaluation on synthetic and real-life logs shows that our method is fast and accurate in the detection of typical change patterns, and performs significantly better than the state of the art.

Characterizing Drift from Event Streams of Business Processes

Early detection of business process drifts from event logs enables analysts to identify changes that may negatively affect process performance. However, detecting a process drift without characterizing its nature is not enough to support analysts in understanding and rectifying process performance issues. We propose a method to characterize process drifts from event streams, in terms of the behavioral relations that are modified by the drift. The method builds upon a technique for online drift detection, and relies on a statistical test to select the behavioral relations extracted from the stream that have the highest explanatory power. The selected relations are then mapped to typical change patterns to explain the detected drifts. An extensive evaluation on synthetic and real-life logs shows that our method is fast and accurate in characterizing process drifts, and performs significantly better than alternative techniques.

Discovering Causal Factors Explaining Business Process Performance Variation

Business process performance may be affected by a range of factors, such as the volume and characteristics of ongoing cases or the performance and availability of individual resources. Event logs collected by modern information systems provide a wealth of data about the execution of business processes. However, extracting root causes for performance issues from these event logs is a major challenge. Processes may change continuously due to internal and external factors. Moreover, there may be many resources and case attributes influencing performance. This paper introduces a novel approach based on time series analysis to detect cause-effect relations between a range of business process characteristics and process performance indicators. The scalability and practical relevance of the approach has been validated by a case study involving a real-life insurance claims handling process.

Detecting Sudden and Gradual Drifts in Business Processes from Execution Traces

Business processes are prone to unexpected changes, as process workers may suddenly or gradually start executing a process differently in order to adjust to changes in workload, season, or other external factors. Early detection of business process changes enables managers to identify and act upon changes that may otherwise affect process performance. Business process drift detection refers to a family of methods to detect changes in a business process by analyzing event logs extracted from the systems that support the execution of the process. Existing methods for business process drift detection are based on an explorative analysis of a potentially large feature space and in some cases they require users to manually identify specific features that characterize the drift. Depending on the explored feature space, these methods miss various types of changes. Moreover, they are either designed to detect sudden drifts or gradual drifts but not both. This paper proposes an automated and statistically grounded method for detecting sudden and gradual business process drifts under a unified framework. An empirical evaluation shows that the method detects typical change patterns with significantly higher accuracy and lower detection delay than existing methods, while accurately distinguishing between sudden and gradual drifts.

Services composition in IMS environment: An evolved SCIM based approach

IP Multimedia Subsystem (IMS) has been widely accepted by telecom industry as a prolific platform for providing next generation Telecom services, and massive deployment has been finally taking off. However, most of the services in IMS environment are still developed in silo method which makes new services implementation to be a time-consuming and burdensome process. In this paper, we consider service composition as an alternative approach for rapid service creation, reusing service capabilities to implement new services. For this goal, we analyze existing standards for IMS service composition management, and show how the Service Capability Interaction Manager (SCIM) is used for brokering services capabilities and managing service composition. Based on this analysis, we propose a SCIM-based architecture evolution in order to enable an agile service composition.

Enhancing navigation in virtual worlds through social networks analysis

Although Virtual Worlds (VWs) are exponentially gaining popularity, they remain digitalized environments allowing to users only basic interactions and limited experience of life due mainly to the lack of realism and immersion. Thus more and more research initiatives are trying to make VWs more realistic through, for example, the use of haptic equipments and high definition drawing. This paper presents a new contribution towards enhancing VWs realism from the visual perception perspective by performing social networks analysis and conditioning avatars rendering according to social proximities.