Conformance checking is a crucial aspect of process mining, enabling organizations to identify deviations between actual process behavior and modeled expectations. At the heart of conformance checking lies the concept of optimal alignments, which provide a detailed, cost-minimized mapping of observed behavior to expected behavior. Optimal alignments facilitate the identification of root causes of non-conformity and guide corrective actions. This is a critical area where Artificial Intelligence (AI) plays a pivotal role in driving effective process improvement. However, computing optimal alignments poses significant computational challenges due to the vast search space inherent in declarative process models. Consequently, existing approaches often struggle with scalability and efficiency, limiting their applicability in real-world settings. This paper introduces DeclareAligner , a novel algorithm that uses the A* search algorithm, an established AI pathfinding technique, to tackle the problem from a fresh perspective leveraging the flexibility of declarative models. Key features of DeclareAligner include only performing actions that actively contribute to fixing constraint violations, utilizing a tailored heuristic to navigate towards optimal solutions, and employing early pruning to eliminate unproductive branches, while also streamlining the process through preprocessing and consolidating multiple fixes into unified actions. The proposed method is evaluated using 8054 synthetic and real-life alignment problems, demonstrating its ability to efficiently compute optimal alignments by significantly outperforming the current state of the art. By enabling process analysts to more effectively identify and understand conformance issues, DeclareAligner has the potential to drive meaningful process improvement and management.

Changes, planned or unexpected, are common during the execution of real-life processes. Detecting these changes is a must for optimizing the performance of organizations running such processes. Most of the algorithms present in the state-of-the-art focus on the detection of sudden changes, leaving aside other types of changes. In this article, we will focus on the automatic detection of gradual drifts, a special type of change, in which the cases of two models overlap during a period of time. The proposed algorithm relies on conformance checking metrics to carry out the automatic detection of the changes, performing also a fully automatic classification of these changes into sudden or gradual. The approach has been validated with a synthetic dataset consisting of 120 logs with different distributions of changes, getting better results in terms of detection and classification accuracy, delay, and change region overlapping than the main state-of-the-art algorithms.

Conformance checking techniques compare how a process is supposed to be executed according to a model with how it is executed in reality according to an event log. Alignment-based approaches are the most successful solutions for conformance checking. Optimal alignments are a way of finding the best match between the real and the modeled behavior and identifying the differences. However, finding these optimal alignments is a challenging task, especially for complex cases where the log and the model have many events and paths. The difficulty lies in the computational complexity required to find these alignments. To address this problem, we propose an efficient algorithm named REACH based on the A* search algorithm. The core components of the proposal are the use of a partial reachability graph for faster execution of process models for alignment computation and a set of optimization techniques for reducing the number of states explored by the A* algorithm. These improve performance by both reducing the required computation time per state and the number of states to process respectively. To evaluate the performance and scalability, we conducted tests using 227 pairs of logs and models, comparing the results obtained with those from 10 state-of-the-art approaches. Results show that REACH outperforms the other proposals in runtimes, and even aligns logs and models that no other algorithm is able to align.

Predictive monitoring of business processes is a subfield of process mining that aims to predict, among other things, the characteristics of the next event or the sequence of the next events. Although multiple approaches based on deep learning have been proposed, mainly recurrent neural networks and convolutional neural networks, none of them really exploit the structural information available in process models. This paper proposes an approach that simultaneously learns spatio-temporal information from both the event log and the process model by combining recurrent neural networks with graph convolutional networks. Thus, common patterns from process models, such as loops or parallels, can be learned while avoiding overwriting information during the encoding phase. An experimental evaluation of real-life event logs shows that our approach is more consistent and outperforms the current state-of-the-art approaches.

Process mining techniques need to know not only the activity that is recorded at each moment but also its execution context, i.e., how the activity is related to the rest of process activities. This information is very relevant for addressing many process mining problems, such as predictive monitoring, especially when applying deep learning techniques. However, the representation of this execution context has not received much attention in the literature. In this paper, we present a novel approach based on an autoencoder for the generation and training of embeddings that properly capture the contextual information related to the execution of an activity. This deep learning-based architecture allows to obtain representations at the level of individual activity, roles, or any other categorical attributes. To evaluate this proposal, an experimentation has been conducted that shows how the embeddings generated with the proposed autoencoder allow to improve the accuracy of the next activity prediction with three of the main state-of-the-art predictive models. Particularly noteworthy are the results on complex processes, where the contextual information provided by our embeddings can significantly improve the state-of-the-art results.

Real life business processes change over time, in both planned and unexpected ways. The detection of these changes is crucial for organizations to ensure that the expected and the real behavior are as similar as possible. These changes over time are called concept drifts and its detection is a big challenge in process mining since the inherent complexity of the data makes difficult distinguishing between a change and an anomalous execution. In this article, we presentC2D2(Conformance Checking-based Drift Detection), a new approach to detect sudden control-flow changes in the process models from event traces.C2D2combines discovery techniques with conformance checking methods to perform an offline detection. Our approach has been validated with a synthetic benchmarking dataset formed by 68 logs, showing an improvement in the accuracy while maintaining a very low delay in the drift detection.

Predictive monitoring of business processes is concerned with the prediction of ongoing cases on a business process. Lately, the popularity of deep learning techniques has propitiated an ever-growing set of approaches focused on predictive monitoring based on these techniques. However, the high disparity of process logs and experimental setups used to evaluate these approaches makes it especially difficult to make a fair comparison. Furthermore, it also difficults the selection of the most suitable approach to solve a specific problem. In this paper, we provide both a systematic literature review of approaches that use deep learning to tackle the predictive monitoring tasks. In addition, we performed an exhaustive experimental evaluation of 10 different approaches over 12 publicly available process logs.

Automated process discovery is a process mining operation that takes as input an event log of a business process and generates a diagrammatic representation of the process. In this setting, a common diagrammatic representation generated by commercial tools is the directly-follows graph (DFG). In some real-life scenarios, the DFG of an event log contains hundreds of edges, hindering its understandability. To overcome this shortcoming, process mining tools generally offer the possibility of filtering the edges in the DFG. We study the problem of efficiently filtering the DFG extracted from an event log while retaining the most frequent relations. We formalize this problem as an optimization problem, specifically, the problem of finding a sound spanning subgraph of a DFG with a minimal number of edges and a maximal sum of edge frequencies. We show that this problem is an instance of an NP-hard problem and outline several polynomial-time heuristics to compute approximate solutions. Finally, we report on an evaluation of the efficiency and optimality of the proposed heuristics using 13 real-life event logs.

In this paper, we propose a framework for the automatic generation of natural language descriptions of healthcare processes using quantitative and qualitative data and medical expert knowledge. Inspired by the demand of novel ways of conveying process mining analysis results of healthcare processes (Rojas et al., 2016), our framework is based on the most widely used Data-To-Text (D2T) pipeline (Reiter, 2007) and on the usage of process mining techniques. Backed by a general model that handles process data, this framework is able to quantify attributes in time during a process life-span, recall temporal relations and waiting times between events and its possible causes and compare case (patient) attributes between groups, among other features. Through integrating fuzzy quantification techniques, our framework is able to represent relevant quantitative process information with some degree of uncertainty present on it and describe it in natural language involving uncertain terms. A real application over the Aortic Stenosis Integrated Care Process of the University Hospital of Santiago de Compostela is presented, showcasing the potential of our framework for providing natural language descriptions of healthcare processes addressed to medical experts. Following the standards of D2T systems, manual human validation was conducted for the generated natural language descriptions by fifteen medical experts in Cardiology. Validation results are very positive, since a global average of 4.07/5.00 was achieved for questions related to understandability, usefulness and impact of the natural language descriptions on the medical experts work. More precisely, results indicate i) that the modality which conveyed the information most efficiently was natural language ii) a very clear preference of texts over the usual graphic representation of process information as the way for conveying information to experts (4.28/5.00), and iii) natural language descriptions provide relevant and useful information about the process, allowing for its improvement.

Abstract In this work, we present a complete system to produce an automatic linguistic reporting about the customer activity patterns inside open malls, a mixed distribution of classical malls joined with the shops on the street. These reports can assist to design marketing campaigns by means of identifying the best places to catch the attention of customers. Activity patterns are estimated with process mining techniques and the key information of localization. Localization is obtained with a parallelized solution based on WiFi fingerprint system to speed up the solution. In agreement with the best practices for human evaluation of natural language generation systems, the linguistic quality of the generated report was evaluated by 41 experts who filled in an online questionnaire. Results are encouraging, since the average global score of the linguistic quality dimension is 6.17 (0.76 of standard deviation) in a 7-point Likert scale. This expresses a high degree of satisfaction of the generated reports and validates the adequacy of automatic natural language textual reports as a complementary tool to process model visualization.

In this paper, we propose a series of fuzzy temporal protoforms in the framework of the automatic generation of quantitative and qualitative natural language descriptions of processes. The model includes temporal and causal information from processes and attributes, quantifies attributes in time during the process life-span and recalls causal relations and temporal distances between events, among other features. Through integrating process mining techniques and fuzzy sets within the usual Data-to-Text architecture, our framework is able to extract relevant quantitative temporal as well as structural information from a process and describe it in natural language involving uncertain terms. A real use-case in the cardiology domain is presented, showing the potential of our model for providing natural language explanations addressed to domain experts.

Self-regulated learning involves students taking the responsibility of their own learning. Self-regulated learning students usually adopt a variety of learning strategies and behaviors, such as the performance of forethought-performance-reflection cycles or the regular and sequenced work over time, that eventually enable them to achieve a more significant and long-lasting learning. In this paper, we explore if these particular behaviors and strategies can be analyzed through the application of process mining techniques taking as data the events registered during the performance of learning activities. The discovery of the underlying processes followed by students can open new approaches to study the real self-regulated strategies used by students. The paper reviews the techniques and tools available to perform the process mining of events related to self-regulated learning and describes some initial works in this area. Furthermore, as an initial empirical study, we analyze the process followed by students regarding the visualization of videos provided in a first-year engineering subject. The obtained results are studied taking into account the grades obtained by the students. The results show that the students that obtained the best grades follow more varied routes than the students that obtained the worst grades. In addition, the best ones are more regular over time regarding weekly video visualization, mainly at the beginning of the term, while the worst ones visualize the videos mainly at the second part of the term.

Learning Analytics (LA) is currently the most effective way of achieving better information and in-depth insights of the learning processes. Specifications like Experience API (xAPI) have been defined as part of LA initiatives to add interoperability among LA-aware applications. Tools that validate the conformance of data to these specifications are key components to assure the interoperability among applications. In this paper, a comparative evaluation of relevant validation tools of the xAPI specification is presented. This comparison focus specially on the structural and semantic features of the xAPI specification, revealing that most of the currently available tools do not support the semantic constraints of the specification.

In this paper we present a hybrid approach for automatic composition of Web services that generates semantic input-output based compositions with optimal end-to-end QoS, minimizing the number of services of the resulting composition. The proposed approach has four main steps: (1) generation of the composition graph for a request; (2) computation of the optimal composition that minimizes a single objective QoS function; (3) multi-step optimizations to reduce the search space by identifying equivalent and dominated services; and (4) hybrid local-global search to extract the optimal QoS with the minimum number of services. An extensive validation with the datasets of the Web Service Challenge 2009-2010 and randomly generated datasets shows that: (1) the combination of local and global optimization is a general and powerful technique to extract optimal compositions in diverse scenarios; and (2) the hybrid strategy performs better than the state-of-the-art, obtaining solutions with less services and optimal QoS.

In this paper we present a theoretical analysis of graph-based service composition in terms of its dependency with service discovery. Driven by this analysis we define a composition framework by means of integration with fine-grained I/O service discovery that enables the generation of a graph-based composition which contains the set of services that are semantically relevant for an input-output request. The proposed framework also includes an optimal composition search algorithm to extract the best composition from the graph minimising the length and the number of services, and different graph optimisations to improve the scalability of the system. A practical implementation used for the empirical analysis is also provided. This analysis proves the scalability and flexibility of our proposal and provides insights on how integrated composition systems can be designed in order to achieve good performance in real scenarios for the web.

In this paper, we present a big data software architecture that uses an ontology, based on the Experience API specification, to semantically represent the data streams generated by the learners when they undertake the learning activities of a course, e.g., in a course. These data are stored in a RDF database to provide a high performance access so learning analytics services can process the large amount of data generated in a virtual learning environment. These services provide valuable information to teachers and instructors such as predict the learner's performance, discover the real learning paths, extract the learner's behavior patterns and so on. The proposed architecture has been validated in the Educational Technology undergraduate course of the Degree in Pedagogy at the Faculty of Education of the University of Santiago de Compostela.

The Open Educational Resource concept was proposed several years ago as a way to develop the Open Movement principles in the educational domain. Despite the open and participative view underlying this proposal, there has not been a significant adoption of this concept by users. Many problems and barriers have been considered and analyzed. Nevertheless, we consider that a main issue is the contradiction among the definitions proposed and the real support provided by platforms. In addition, there exist also different approaches to the concept by users and organizations. In this paper we call for a real open approach to the Open Educational Resource concept and introduce a platform where specific functionalities have been introduced to support its development.

In this paper we present the Soft Learn Activity Reporter (SLAR) service which automatically generates textual short-term reports about learners' behavior in virtual learning environments. Through this approach, we show how textual reporting is a coherent way of providing information that can complement (and even enhance) visual statistics and help teachers to understand in a comprehensible manner the behavior of their students during the course. This solution extracts relevant information from the students' activity and encodes it into intermediate descriptions using linguistic variables and temporal references, which are subsequently translated into texts in natural language. The examples of application on real data from an undergraduate course supported by the Soft Learn platform show that automatic textual reporting is a valuable complementary tool for explaining teachers and learners the information comprised in a Learning Analytics Dashboard.

Capturing and storing learners' data is the first step to implement a learning analytics architecture. The Experience API (xAPI) specification is a de facto standard that describes (i) a REST-based API to store and retrieve the learners' activity data, and (ii) an RDF-based data model where the restrictions among data are specified in natural language. In this paper, we present an ontology that formally represents both the data model and the restrictions of the xAPI specification, facilitating the conformance testing of the data coming from the learning management system to guarantee the compliance with the specification.

This paper presents a hybrid approach for automatic composition of Web services that generates semantic input-output matching compositions minimizing the number of services and optimizing the global QoS. The proposed approach has four main steps: 1) generation of the composition graph for a request, 2) computation of the optimal QoS of the composition graph, 3) multi-step optimizations of the graph to identify equivalent and dominated services, and 4) hybrid local-global search to extract the optimal QoS with the minimum number of services. A validation with the datasets of the Web Service Challenge 2009-2010 is also provided.

SUMMARY The Universia repository is composed of more than 15 million of educational resources. The lack of metadata describing these resources complicates their classification, search and recovery. To overcome this drawback, it was decided to semantically annotate the available educational resources using the ADEGA algorithm. For this objective, we selected the DBpedia, a cross‐domain linked data composed of more than 3.77 million ‘things’ with 400 million ‘facts’, in order to make sure that the wide range of Universia topics are covered by the ontology. However, this kind of process is extremely expensive from a computational point of view: more than 1600 years of CPU time was estimated to achieve it. In this paper, parallel programming techniques and distributed computing paradigms are combined in order to achieve this semantic annotation in a reasonable time. The cornerstone of this proposal is a resource management and execution framework able to integrate heterogeneous computing resources at our disposal (grid, cluster and cloud resources). As a result, the problem was solved in less than 180 days, demonstrating that it is perfectly feasible to exploit the advantages of these computing models in the field of linked data. Copyright © 2014 John Wiley & Sons, Ltd.

Edu-AREA is a Web 2.0 application whose main goal is to contribute to teaching innovation through open educational resources, activities and experiences. AREA provides information in the form of descriptions of educational resources and guidelines of activities that can be used by teachers to create their teaching guides and later to document their classroom experiencies. Teachers can explore the resources, activities, guides and experiences available, select the ones they prefer, create a copy and modify them adapting to their particular teaching context in accordance with a real open approach. At the current stage of the Edu-AREA development, a main issue is related to the management and classification of information provided by users. This paper introduces a folksonomy approach to tackle with this problem and analyses how to apply it in several elements of the Edu-AREA data model. In addition, a general view of the software architecture is provided.

Prediction of students' performance is one of the most explored issues in educational data mining. To predict if students will achieve the outcomes of the subject based on the previous results enables teachers to adapt the learning design of the subject to the teaching-learning process. However, this adaptation is even more relevant if we could predict the fulfillment of the educational objectives of a subject, since teachers should focus the adaptation on the learning resources and activities related to those educational objectives. In this paper, we present an experiment where a support vector machine is applied as a classifier that predicts if the different educational objectives of a subject are achieved or not. The inputs of the problem are the marks obtained by the students in the questionnaires related to the learning activities that students must undertake during the course. The results are very good, since the classifiers predict the achievement of the educational objectives with precision over 80%.

In self-regulated learning, evaluation is a complex task of the teaching process, but even more if students have social media that allow them to build their personal learning environment in different ways. In these kind of virtual environments a large amount of data that needs to be assessed by teachers is generated, and therefore they require tools that facilitate the assessment task. In this paper, we present an experiment with a process mining-based learning analytics tool, called SoftLearn, that helps teachers to assess the student's activity in self-regulated learning. The subject of this experiment is taught in blended learning mode with weekly classroom sessions, and the students use a social network software, called ELGG, as an e-portfolio in which they reflect their individual knowledge process construction. The results show that the use of this tool reduces significantly the assessment time and helps teachers to understand the learning process of the students.

One of the most challenging issues in learning analytics is the development of techniques and tools that facilitate the evaluation of the learning activities carried out by learners. In this paper, we faced this issue through a process mining-based platform, called Soft Learn, that is able to discover complete, precise and simple learning paths from event logs. This platform has a graphical interface that allows teachers to better understand the real learning paths undertaken by learners.

In this paper an approach for taking advantage of the information available in the social network tools of Learning Management Systems is presented. Specifically, our objective is to determine domain experts from their participation in the social networks. Our approach first integrates the different sources of information through an ontology based on the Dublin Core Metadata, and then applies Semantic Web technology to filter and classify each one of the social resources, so providing the basis for a better precision in the information retrieval process. The solution has been implemented following the paradigm of service oriented architecture.

This paper presents a recommendation algorithm that suggests relevant collaborative teams of teachers to the coordinators of courses or units of learning. This work is motivated by the need of automating and facilitating the search of experts that could contribute to the design and development of units of learning in collaborative authoring tools. The proposed algorithm solves a set covering problem by following a greedy approach that maximizes a Gaussian cost function. The results show that the recommended collaborative teams are optimal in terms of precision, but should be improved in terms of coverage and recall.

Learning objects have arisen in response to the need of high-quality and reusable instructional materials. The repositories that hold learning objects allow educators to create and share their instructional contents in an organized infrastructure and where information should be easily searchable. Therefore, a key point of these repositories is the way learning objects are categorized. In this paper, we present an approach for classifying semantically learning objects whose metadata are described in Dublin Core. Specifically, our objective is to annotate automatically the subject of the learning object with instances of the DBpedia ontology, that is, to annotate the learning objects repository with linked open data. The syntactic and semantic analysis of the learning object will drive the selection of the most appropriate categories in the linked data repository.

Nowadays a greater number of domains require an improvement in the flexibility of its e-learning systems to so facilitate the adaptation of their course to their user needs. Some adaptive learning systems have been developed in recent years to cover part of these needs, and provide the ability to redirect the student learning flow based on the knowledge base (mainly rules) defined by the teacher. However, these systems, most of them based on the IMS Learning Design specification, have a major restriction: once the course has been published, it can not be changed, regardless that the circumstances in which the course is taught have changed. To remedy this lack, in this paper we present a solution to adapt the structure of units of learning at runtime. This solution extends a proposal for modelling IMS Learning Design through workflows with new capabilities that facilitate the replacement of plays, events and activities on the fly.

Service Oriented Architectures and web service technology are becoming popular in recent years. As more web services can be used over the Internet, the need to find efficient algorithms for web services composition that can deal with large amounts of services becomes important. These algorithms must deal with different issues like performance, semantics or user restrictions. In this paper we present an A* algorithm which solves the problem of semantic input-output message structure matching for web service composition. Given are quest, a service dependency graph with a subset of the original services from an external repository is dynamically generated. Then, the A* search algorithm is used to find a minimal composition that satisfies the user request. Moreover, in order to improve the performance, a set of dynamic optimization techniques has been implemented over the search process. A full experimental validation with eight different public repositories has been done showing a good performance as in all tests as the algorithm finds a valid solution with minimal number of services and execution path.

In this paper we present an approach for improving a specific class of semantic annotation, that relates a term of the document with a (sub)tree of the ontology, instead of linking a term with a single concept of the ontology. An important part of this class of annotation is filtering the relevant (sub)nodes and relations, because the returned graph should only contain relevant information, that is, nodes that are truly related with the topics of the document. In addition, we consider that the relevance of nodes vary depending on if the node is a branch or a leaf, that is, if the node has links to other nodes or it is a text-based description. This paper focuses on the relevance of branch nodes, which is calculated from the relevance of its links, since leaf nodes relevance is usually estimated by similarity metrics. Specifically, our approach incises in learning (through a genetic algorithm) and assigning the most appropriate weights to these links in order to reduce the precision/recall curve of the annotation process. The results show that our solution is viable and outperforms the state of the art approaches.

In this paper we propose two novel multicriteria recommendation algorithms and present a comparison with other recommendation approaches in the gastronomic domain. The motivation comes from the fact that traditional single criterion approaches consider that two users share the same taste when they provide similar global ratings on the experienced items. However, these users could agree on global ratings while having completely different priorities on item attributes and different preferences on attribute values. Multicriteria recommenders seem to be a promising solution for this problem as they aggregate user ratings on several item components in order to generate more accurate recommendations. Experiments conducted on Santiago(e)Tapas, a real gastronomic contest where customers evaluate different aspects of several restaurants, demonstrate that one of our algorithms, Support Distance Weighting, outperforms other multi-criteria and single-criterion algorithms in terms of prediction precision.

This paper describes a service-oriented architecture that facilitates the execution of learning units, based on the IMS Learning Design specification (IMS LD), in a metaverse platform like Second Life or OpenSim. This architecture ex-ternalizes the functionalities of the IMS LD engine through web services that are invoked by metaverse platform scripts. A software module that translates the requests of the virtual platform has been developed in order to facilitate this invocation and a virtual teaching scenario has been designed to test the feasibility of this architecture.

In this paper an ontology-based Petri net engine to execute units of learning based on the IMS Learning Design (IMS LD) specification is presented. This engine has a layered architecture that enables the execution of IMS LD learning flows by means of high-level Petri nets representing the semantics of the IMS LD dynamic elements. Even if changes in the specification were made, the engine would not have to be re-implemented, and only the introduction of new rules and/or axioms in the ontology would required. Moreover, a service-oriented architecture has been implemented for allowing web-based learning management systems to access to the IMS LD engine.

Web services are interfaces that describe a collection of operations that are network-accessible through standardized Web protocols. When a required operation is not found, several services can be compounded to get a composite service that performs the desired task. To find this composite service, a search process over a huge search space must be performed. The algorithm that composes the services must select the adequate atomic processes and, also, must choose the correct way to combine them using the different available control structures. In this paper a genetic programming algorithm for Web services composition is presented. The algorithm has a context-free grammar to generate the valid structures of the composite services. Moreover, it includes a method to update the attributes of each node. A full experimental validation with a repository of 1,000 Web services has been done, showing a great performance as the algorithm finds a valid solution in all the tests.

Machine scheduling is a critical problem in industries where products are custom-designed. The wide range of products, the lack of previous experiences in manufacturing, and the several conflicting criteria used to evaluate the quality of the schedules define a huge search space. Furthermore, production complexity and human influence in each manufacturing step make time estimations difficult to obtain thus reducing accuracy of schedules. The solution described in this paper combines evolutionary computing and neural networks to reduce the impact of (i) the huge search space that the multi-objective optimization must deal with and (ii) the inherent problem of computing the processing times in a domain like custom manufacturing. Our hybrid approach obtains near optimal schedules through the Non-dominated Sorting Genetic Algorithm II (NSGA-II) combined with time estimations based on multilayer perceptron networks.

This paper presents WebLD, a web authoring tool to create units of learning following the IMS Learning Design (IMS LD) specification. WebLD presents two main features that distinguish it from other tools: (1) it is a Web application, therefore available anytime, anywhere from the Internet, and (2) it includes semantic technology, based on our IMS LD ontology, that enables the automatic validation of IMS LD documents.

This paper presents an ontology describing the semantics of the level B of the IMS learning design specification. The motivation for this work comes from the need (1) to improve the semantic description of units of learning (UoL) based on IMS LD, and (2) to overcome the limitations of UoLs merely described by the IMS LD level A. The ontology includes a taxonomy of concepts and a set of axioms formalized in first order logic. It was represented in F-logic and OWL

This paper briefly describes how ontologies can be used to model, validate and execute IMS learning design. The main contribution relies on incorporate the implicit knowledge found on the IMS LD specification in terms of formal axioms

This paper describes how the Astronomy case study is modelled and implemented using a IMS-LD ontology

This work describes the design and implementation of a knowledge based system that automatizes the furniture budgeting task. The aim of this task is to estimate the production cost that would suppose a furniture order manufacturing. Fulfillment of this aim requires the management of the company resources, information and knowledge in order to determine workload and financial impacts of client request manufacturing. Task modelling has been done through the workflows formalism approach and describes working activities and its relationships with the company resources. The system software architecture is based on workflow modeling and is an adjustment of a conceptual architecture proposed to be a standard for workflows systems.

We present a teacher interface for a computer supported educational system that we are developing. This interface was developed over a PDA with wireless network connectivity to be carried by the teacher into the classroom. The first prototype implemented an interface with a virtual keyboard and mouse in order to remotely manage the classroom server and its peripherals. Additionally other features were included, such a remote video projector control and an agenda application. However, users had problems with regard to direct text introduction and mouse sensibility. Knowledge of the domain was acquired from experts in the field of education, and as a result, a new set of project requirements were defined and a second version of the interface has been developed. Its task-oriented design improves usability through minimizing the need for a virtual keyboard and mouse utilization. This system is currently in use in two classrooms in our university.

In this work we present the design proposal for a therapeutic specialist TS in the domain of the intensive coronary care unit ICCU for situations of acute myocardial infarct AMI . This TS can be placed within a more general framework of the intelligent supervision of patients interned in an ICCU, and it carries out two basic functions: 1 recommending to the physician that therapy should be administered according to the state and response of the treatment in progress; and 2 to continually monitor the evolution of the patient with the therapy in progress, advising the physician of necessary therapeutic modifications associations with other drugs, suspension of treatment, etc. in order to compensate for the evolution of the patient and aid his recovery. Thus we present a specialist agent architecture designed to carry out both functions, which is based on the identification, design, and implementation of therapeutic tasks, on the explicit separation between knowledge of the domain and the metaknowledge necessary in order for the coordinated accomplishment of these tasks, and on mimicking the methodology followed by the physician when evaluating the monitoring of the patient's condition.