Juan Carlos Corrales

Ranking BPEL Processes for Service Discovery

Finding useful services is a challenging and important task in several applications. Current approaches for services retrieval are mostly limited to the matching of their inputs/outputs. In this paper, we argue that in several applications (services having multiple and dependent operations and scientific workflows), the service discovery should be based on the specification of service behavior. The idea behind is to develop matching techniques that operate on behavior models and allow delivery of approximate matches and evaluation of semantic distance between these matches and the user requirements. To do so, we reduce the problem of behavioral matching to a graph matching problem and adapt existing algorithms for this purpose. To validate our approach, we developed a BPEL ranking platform that allows to find in a service repository, a set of service candidates satisfying user requirements, and then, to rank these candidates using a behavioral-based similarity measure.

Behavioral matchmaking for service retrieval

The capability to easily find useful services (software applications, software components, scientific computations) becomes increasingly critical in several fields. Current approaches for services retrieval are mostly limited to the matching of their inputs/outputs. Recent works have demonstrated that this approach is not sufficient to discover relevant components. In this paper we argue that, in many situations, the service discovery should be based on the specification of service behavior (in particular, the conversation protocol). The idea behind is to develop matching techniques that operate on behavior models and allow delivery of partial matches and evaluation of semantic distance between these matches and the user requirements. Consequently, even if a service satisfying exactly the user requirements does not exist, the most similar ones will be retrieved and proposed for reuse by extension or modification. To do so, we reduce the problem of behavioral matching to a graph matching problem and we adapt existing algorithms for this purpose. A prototype is presented (available as a Web service) which takes as input two conversation protocols and evaluates the semantic distance between them; the prototype provides also the script of edit operations that can be used to alter the first model to render it identical with the second one

BPEL processes matchmaking for service discovery

The capability to easily find useful services (software applications, software components, scientific computations) becomes increasingly critical in several fields Current approaches for services retrieval are mostly limited to the matching of their inputs/outputs Recent works have demonstrated that this approach is not sufficient to discover relevant components In this paper we argue that, in many situations, the service discovery should be based on the specification of service behavior The idea behind is to develop matching techniques that operate on behavior models and allow delivery of partial matches and evaluation of semantic distance between these matches and the user requirements Consequently, even if a service satisfying exactly the user requirements does not exist, the most similar ones will be retrieved and proposed for reuse by extension or modification To do so, we reduce the problem of behavioral matching to a graph matching problem and we adapt existing algorithms for this purpose A prototype is presented which takes as input two BPEL models and evaluates the semantic distance between them; the prototype provides also the script of edit operations that can be used to alter the first model to render it identical with the second one.

Behavioral matchmaking for service retrieval: Application to conversation protocols

The capability to easily find useful services (software applications, software components, scientific computations) becomes increasingly critical in several fields. Current approaches for services retrieval are mostly limited to the matching of their inputs/outputs possibly enhanced with some ontological knowledge. Recent works have demonstrated that this approach is not sufficient to discover relevant components. In this paper we argue that, in many situations, the service discovery should be based on the specification of service behavior (in particular, the conversation protocol). The idea behind is to develop matching techniques that operate on behavior models and allow delivery of partial matches and evaluation of semantic distance between these matches and the user requirements. Consequently, even if a service satisfying exactly the user requirements does not exist, the most similar ones will be retrieved and proposed for reuse by extension or modification. To do so, we reduce the problem of behavioral matching to a graph matching problem and we adapt existing algorithms for this purpose. A prototype is presented (available as a web service) which takes as input two conversation protocols and evaluates the semantic distance between them.

BeMatch: a platform for matchmaking service behavior models

The capability to easily find useful services (software applications, software components, scientific computations) becomes increasingly critical in several fields. Current approaches for services retrieval are mostly limited to the matching of their inputs/outputs possibly enhanced with some ontological knowledge. Recent works have demonstrated that this approach is not sufficient to discover relevant components. Motivated by these concerns, we have developed BeMatch platform for ranking web services based on behavior matchmaking. We developed matching techniques that operate on behavior models and allow delivery of partial matches and evaluation of semantic distance between these matches and user requirements. Consequently, even if a service satisfying exactly the user requirements does not exist, the most similar ones will be retrieved and proposed for reuse by extension or modification. We exemplify our approach for behavioral services matchmaking by describing two demonstration scenarios for matchmaking BPEL and WSCL protocols, respectively. A demo scenario is also described concerning the tool for evaluating the effectiveness of the behavioral matchmaking method.

Automated context aware composition of Advanced Telecom Services for environmental early warnings

This paper presents one of the main components of a framework for automated composition of Advanced Telecom Services for environmental early Warnings. The framework, called AUTO, is composed by three main modules: a request processing module that transforms natural language and context information into a planning instance; the automated planning module, based on PELEA, an architecture for planning and execution; and the Service Execution Environment Advance Telecom Services. This paper focuses on the description of the translation of the user request in natural language and his context into planning instances. These planning instances represent service composition tasks based on Automated Planning. The advantages of this approach, like the automatic inclusion of context and user preferences in the composition of services, will be presented. Also, the current implementation will be described and some experimentation will prove the viability of AUTO.

An Automated User-Centered Planning Framework for Decision Support in Environmental Early Warnings

This paper presents the integration of automated planning in AUTO, a framework able to design plans of composed services for environmental early warning management. AUTO is based on three components: a request processing module that transforms natural language and context information into a planning instance; the automated planning and execution module based on an architecture for planning and execution, PELEA; and the Service Execution Environment for Web and Telco Services. The integration of a planning component provides two basic functionalities: the possibility of customizing the composition of services based on the preferences of the user and a middleware level that interfaces the execution of services in the environment.

Graph Patterns as Representation of Rules Extracted from Decision Trees for Coffee Rust Detection

Diseases in Agricultural Production Systems represent one of the biggest drivers of losses and poor quality products. In the case of coffee production, experts in this area believe that weather conditions, along with physical properties of the crop are the main variables that determine the development of a disease known as Coffee Rust. On the other hand, several Artificial Intelligence techniques allow the analysis of agricultural environment variables in order to obtain their relationship with specific problems, such as diseases in crops. In this paper an extraction of rules to detect rust in coffee from induction of decision trees and expert knowledge is addressed. Finally, a graph-based representation of these rules is submitted, in order to obtain a model with greater expressiveness and interpretability.

An Empirical Multi-classifier for Coffee Rust Detection in Colombian Crops

Rust is a disease that leads to considerable losses in the worldwide coffee industry. In Colombia, the disease was first reported in 1983 in the department of Caldas. Since then, it spread rapidly through all other coffee departments in the country. Recent research efforts focus on detection of disease incidence using computer science techniques such as supervised learning algorithms. However, a number of different authors demonstrate that results are not sufficiently accurate using a single classifier. Authors in the computer field propose alternatives for this problem, making use of techniques that combine classifier results. Nevertheless, the traditional approaches have a limited performance due to dataset absence. Therefore we proposed an empirical multi-classifier for coffee rust detection in Colombian crops.

Grouping of business processes models based on an incremental clustering algorithm using fuzzy similarity and multimodal search

A model for searching business processes, based on a multimodal approach that integrates textual and structural information.A clustering mechanism that uses a similarity function based on fuzzy logic for grouping search results.Evaluation of search method using internal quality assessment and external assessment based on human criteria. Nowadays, many companies standardize their operations through Business Process (BP), which are stored in repositories and reused when new functionalities are required. However, finding specific processes may become a cumbersome task due to the large size of these repositories. This paper presents MulTimodalGroup, a model for grouping and searching business processes. The grouping mechanism is built upon a clustering algorithm that uses a similarity function based on fuzzy logic; this grouping is performed using the results of each user request. By its part, the search is based on a multimodal representation that integrates textual and structural information of BP. The assessment of the proposed model was carried out in two phases: 1) internal quality assessment of groups and 2) external assessment of the created groups compared with an ideal set of groups. The assessment was performed using a closed BP collection designed collaboratively by 59 experts. The experimental results in each phase are promising and evidence the validity of the proposed model.