Electrical biosignals, stemming from potential differences in specific tissues like muscles, play a pivotal role in medical diagnoses, aiding in identifying ailments from arrhythmias to brain traumas. The burgeoning realm of Automatic Machine Learning (AutoML), integrating automatic optimization with machine learning, is ushering in novel methodologies to decipher these signals with minimal preliminary data comprehension. This review delves into the contemporary discourse on AutoML techniques tailored for electrical biosignal quandaries, drawing from articles spanning 2018 to 2022 from six technology and machine learning-centric databases. Consequently, insights into biosignal comprehension, the apt AutoML solutions, and prevailing challenges in the domain are delineated.

Named Entity Recognition (NER) is a challenging learning task of identifying and classifying entity mentions in texts into predefined categories. In recent years, deep learning (DL) methods empowered by distributed representations, such as word- and character-level embeddings, have been employed in NER systems. However, for information extraction in Police narrative reports, the performance of a DL-based NER approach is limited due to the presence of fine-grained ambiguous entities. For example, given the narrative report “Anna stole Ada’s car”, imagine that we intend to identify the VICTIM and the ROBBER, two sub-labels of PERSON. Traditional NER systems have limited performance in categorizing entity labels arranged in a hierarchical structure. Furthermore, it is unfeasible to obtain information from knowledge bases to give a disambiguated meaning between the entity mentions and the actual labels. This information must be extracted directly from the context dependencies. In this paper, we deal with the Hierarchical Entity-Label Disambiguation problem in Police reports without the use of knowledge bases. To tackle such a problem, we present HELD, an ensemble model that combines two components for NER: a BLSTM-CRF architecture and a NER tool. Experiments conducted on a real Police reports dataset show that HELD significantly outperforms baseline approaches.

Credit scoring has become a critical tool for financial institutions to discriminate "bad" applicants from "good" ones. One common characteristic of the credit datasets is the imbalance between good and bad applicants, with low defaults (no paid loans). Ensemble classification methodology is widely used in this field. However, dynamic ensemble selection approaches to imbalanced datasets have drawn little consideration. This study aims to measure the performance of the combination of two recent dynamic selection techniques for imbalanced credit scoring datasets, Reduced Minority k-NN (RMkNN) and KNORA-Imbalanced Union (KNORA-IU). We comprehensively evaluate the proposed combination against state-of-the-art competitors on six real-world public datasets and one private one. Experiments show that this combination improves the classification performance on the evaluated datasets in terms of AUC, balanced accuracy, H-measure, G-mean, F-measure, and Recall.

In this article, we consider the External Sensor Trajectory Prediction problem for stolen vehicle trajectories. This analysis brings new challenges to the problem, as crime patterns are dynamic and drivers of stolen vehicles tend to move away from the sensors, which increases data dispersion. We analyze the effectiveness of different machine learning models and propose semantic enrichment with criminal data and points of interest to solve our problem. We also investigate the best attributes to improve EST prediction models, and how different spatial level representations can leverage prediction accuracy.

The movement of criminals is an important factor used in detecting crimes. Individuals sentenced to house arrest who wears an ankle monitor have their trajectories collected periodically. Each offender using an ankle monitor must adhere to a set of rules, for instance, be at his/her home during the night. Unfortunately, some of them break such rules, also some end up committing crimes again. In this demonstration1, we present a prototype system called Crime Monitor to monitor offenders in a semi-open regime. Crime Monitor reports the illegal activities to the police department in real-time based on trajectory features. Thus, the police can effectively prevent crimes from happening and handle them efficiently when they occur. We tackled the trajectory classification problem and used a deep learning model combining embedding with a recurrent neural network to classify illegal activities and learn the pattern regardless of who the criminal user is. We conduct experiments on a real dataset, and we show that DeepeST outperforms other approaches from state- of-the-art.

Abstract During the last few years the volumes of the data that synthesize trajectories have expanded to unparalleled quantities. This growth is challenging traditional trajectory analysis approaches and solutions are sought in other domains. In this work, we focus on data compression techniques with the intention to minimize the size of trajectory data, while, at the same time, minimizing the impact on the trajectory analysis methods. To this extent, we evaluate five lossy compression algorithms: Douglas-Peucker (DP), Time Ratio (TR), Speed Based (SP), Time Ratio Speed Based (TR_SP) and Speed Based Time Ratio (SP_TR). The comparison is performed using four distinct real world datasets against six different dynamically assigned thresholds. The effectiveness of the compression is evaluated using classification techniques and similarity measures. The results showed that there is a trade-off between the compression rate and the achieved quality. The is no “best algorithm” for every case and the choice of the proper compression algorithm is an application-dependent process.

A plethora of applications and devices reporting their locations generate massive amounts of spatiotemporal data along with other useful information. These data can form trajectories with sequences...

Huge volumes of data are produced every day on the Web. These are a big amount of videos, images, and texts that store unstructured information. Text summarization systems were created to facilitate the presentations of large amounts of textual data as well as to aid information retrieval over this type of data. The sentence compression has been developed due to the need for better summaries generated by these systems. However, when trained over domains with restricted amounts of labeled data for sentence compression, neural netword-based models tend to not be able to extract important features. Thus, to improve the performance of these models in this scenario, some pieces of information must be extracted and adapted before being used for training. Thus, we propose a sentence compression model capable of achieving competitive results, even when trained with smaller amounts of data, compared with other neural networkbased models, by using a set of linguistic features extracted from words alongside a rare words reduction strategy over the sentences.

There are relevant rates of violence in Brazil that have increased in recent years. Intelligence and efficiency are required to combat this issue, in order to reduce public money spending and time from public officials. Consequently, this operation may improve the safety of the population. There are several solutions that use intelligent systems to predict where and when a crime will occur, which allows police routes to be sent to areas with a higher risk of danger. In this paper, four machine learning methods are used to predict the location of where a crime will occur in a city of Fortaleza, Brazil. The final result shows that simple algorithms can be efficient in the task of crime prediction. In this paper, the Decision Tree and Bagging Regressor methods obtained the best predictions results.

The task of detecting anomalous data patterns is as important in practical applications as challenging. In the context of spatial data, recognition of unexpected trajectories brings additional difficulties, such as high dimensionality and varying pattern lengths. We aim to tackle such a problem from a probability density estimation point of view, since it provides an unsupervised procedure to identify out of distribution samples. More specifically, we pursue an approach based on normalizing flows, a recent framework that enables complex density estimation from data with neural networks. Our proposal computes exact model likelihood values, an important feature of normalizing flows, for each segment of the trajectory. Then, we aggregate the segments' likelihoods into a single coherent trajectory anomaly score. Such a strategy enables handling possibly large sequences with different lengths. We evaluate our methodology, named aggregated anomaly detection with normalizing flows (GRADINGS), using real world trajectory data and compare it with more traditional anomaly detection techniques. The promising results obtained in the performed computational experiments indicate the feasibility of the GRADINGS, specially the variant that considers autoregressive normalizing flows.

Disorganized urban growth has led cities to chaos, which has brought countless challenges for their development in several sectors, such as traffic organization, public safety, and transportation. Vehicular re-identification (ReID) technologies have become increasingly important in this context since these allow to produce insights capable of benefiting many areas. As recently, methods frequently resorted to Deep Learning and Convolutional Neural Networks (CNNs), especially in the design of loss functions capable of improving the learning capacity of CNNs. Couple and triplet loss techniques have gained prominence, but their effectiveness depends on the mining of samples to converge properly. In this paper, we investigate a new simple approach for vehicle ReID by combining sample mining strategy and approximate nearest neighbor (ANN) method to improve retrieval quality. By relying only on relatively low-dimensional deep features, we were able to obtain state-of-the-art performance on the VeRi-776 dataset in terms of mAP, HIT@1, and HIT@5 metrics, but using relatively simple CNN and ANN methods, which are feasible in CPU for real-time scenarios.

For any Automated Fingerprint Identification System, the quality of its images is vital to ensure the proper accuracy of the whole system. When the quality of an image is not satisfactory, enhancement processes may be applied to help the extraction of the fingerprint features. There are several enhancement techniques, and their suitability depends on the features of the original fingerprint image. Choosing the best enhancement method is crucial because these procedures do not always improve the image quality, and may even worsen it. This work addresses this topic and presents a classifier based on Convolutional Neural Networks (CNNs) that automatically chooses the most suitable enhancement method for a specific image and applies it, but only if necessary. Our solution avoids an excessive human effort to select the best enhancement process and also requires no further training. We evaluated our proposal using FVC's datasets, and results show the benefits of CNN-based feature extractors and that our solution was able to improve the quality of digital printing through the adaptive application of enhancement filters.

The ubiquity of GPS-enabled smartphones and automotive navigation systems allows to monitor and collect massive streams of trajectory data in real-time. This enables real-time analyses on mobility data in urban settings, which in turn have the potential to substantially improve traffic conditions, analyze congested areas, detect events in (quasi) real-time, and so on. While many existing approaches characterize past movements of moving objects from historical trajectory data, or address the problem of finding out clusters of moving objects from data streams, such approaches fail to capture how movement behaviors unravel over time - for instance, they fail to capture typically trafficked routes or traffic jams. In this work we propose NET-CUTiS, a novel approach that addresses the problem of discovering and monitor the evolution of clusters of trajectories over road networks from trajectory data streams. We conduct several experiments that demonstrate the validity of our proposal in terms of clustering quality and run-time performance.

The profitability of banks is highly dependent on credit scoring models, which support decision making to approve a loan to a customer. State-of-the-art credit scoring models are based on learning methods. These methods need to cope with the problem of imbalanced classes since credit scoring datasets usually contain mainly paid loans and few defaults (unpaid ones). Recently, new imbalanced learning techniques have been proposed in the literature, and they can improve the credit scoring results. Motivated by this scenario, we evaluate several classification approaches to credit scoring. Besides, we also assess some preprocessing methods to overcome skewed datasets. To achieve it, we use three public real-world credit scoring datasets. In our experiments, we progressively increase the class imbalance in each of these datasets by randomly undersampling the minority class of defaulters to identify how the predictive power is affected. The results indicate that random forest, extreme gradient boosting perform very well in all imbalance levels. We also find that a complete grid search step can increase the prediction power of classification approaches in high imbalanced datasets.

Credit scoring has become a critical tool to discriminate "bad" applicants from "good" ones for financial institutions. One common characteristic of the credit dataset is the imbalance between good and bad applicants, with low defaults (no paid loans). Ensemble classification methodology is widely used in this field. However, dynamic ensemble selection approaches to imbalanced datasets have drawn little consideration. This study aims to adapt KNORA-Union, an excellent dynamic selection technique, to imbalanced credit scoring problem, the KNORAImbalanced Union (KNORA-IU). In this approach, we propose a new procedure to evaluate the competence of each base classifier. The results, based on four performance measures, indicate that the performance of the KNORA-IU is superior to the state-of-the-art approaches for moderate imbalanced datasets.

The amount of sources and sheer volumes of spatiotemporal data have met an unprecedented growth during the last decade. As a consequence, a rapidly increasing number of applications are seeking to generate value by crunching those data. The development of a system that will tap into the potential value of the spatiotemporal big data analysis for a multitude of applications remains one of the biggest challenges in computer engineering. This paper delves into the key-characteristics of the most prominent suchlike systems. In particular, it provides a thorough analysis of NoSQL datastores as well as a traditional relational database system in terms of their geospatial querying capabilities.

In forensic science, the resolution of crimes is associated with the identification of those involved. In the civil context, the security of automated processes depends on the identification of authorized people. In this sense, fingerprint-based recognition techniques stand out. A fundamental stage is the calculation of the degree of similarity between the samples presented, so the task of identifying a region of interest (ROI), excluding noisy regions, can improve the precision and reduce the computational cost. In this aspect, this work presents a technique of segmentation of the region of interest based on convolutional neural networks (CNN) without pre-processing steps. The new approach was evaluated in two different architectures from state of the art, presenting similarity indexes Distance of Hausdorff (5.92), Dice coefficient (97.28%) and Jaccard Similarity (96.77%) superior to the classic methods. The error rate (3.22%) was better than five segmentation techniques from state of the art and showed better results than another deep learning approach, presenting promising results to identify the region of interest with potential for application in systems based on biometric identification.

In this paper, we predict the movement of objects under the circumstance where external sensors placed on the road-sides (e.g., traffic surveillance cameras) capture their trajectories. This type of trajectories may have very different mobility patterns since they are not restricted to a fleet or a community of users. However, their reported positions are sparse due to the sparsity of the sensor distribution, and incomplete, since the sensors may fail to register the passage of objects. In this paper, we first analyze such external sensor trajectories based on a real dataset, which evidenced the problems of their sparsity and their incompleteness, and hinders the location prediction. In this context, we proposed an approach for coping with the missing data problem. We discussed how to apply this approach in conjunction with the predictors based on Recurrent Neural Networks. In particular, we adjusted the accuracy metrics to account for missing values in the test set, by introducing the distance between the predicted location and the registered next location. We evaluate our approach compared to the baselines, showing an improvement of about 23% in the prediction accuracy while reducing the overall distances. In spite of the contribution of many works in location prediction, at the best of our knowledge, none of those works have studied location prediction for trajectories based on external (road-side) sensors data.

In this demonstration, we present a framework to predict the movement of moving objects under the circumstance where external sensors placed on the road-sides (e.g., traffic surveillance cameras) capture their trajectories. The reported positions in such trajectories are sparse due to the sparsity of the sensor distribution, and incomplete, since the sensors may fail to register the passage of objects. In our framework, we cope with the missing data coming from the external sensor trajectories, which improves the quality of predictions in terms of accuracy and closeness in the road network.

The semantic relationships between heterogeneous data sources are essential for a better understanding of the definitions in the field of Oil Production and Extraction (E&P). The Simple Knowledge Organization System (SKOS) 1 vocabulary can aid in the identification and generation of semantic links between glossaries of terms. We have developed an algorithm that automatically generates mappings with SKOS predicates. The algorithm has shown satisfactory results, especially for the skos:exactMatch predicate, which suggests that automatic methods can be used to improve the quality of terminological mappings.

Time dependent networks are of key importance to allow computing precise travel times taking into consideration moving object's departure time. However the computation of time functions that are used to annotate time dependent networks are challenging since we must cope with noisy and incomplete traffic data. Recent related works adopt approaches that build Piecewise linear functions, which do not cope with aforementioned problems. In this work, we propose a new method for generating Piecewise linear functions by applying a map-matching technique allied to a curve smoothing approach in order to treat outliers and complete data. We performed experiments using real trajectory data and compared our results with a baseline. Preliminary results show that our approach generates time functions with better approximation than the baseline competitor.

The vast diffusion of devices equipped with a GPS receiver has brought the possibility of collecting data related to massive amounts of moving objects on a scale never seen before. During the latest years, such diffusion instigated the development of many different techniques to deal with location prediction problems. Existing works mainly aim at predicting the next location of moving objects by focusing on information in the spatial domain. In this paper we want to take into account information in the temporal domain as well, both to improve the reliability of predictions and to answer not only where a moving object is going to move, but also when an object is expected to leave its current location.

Recent approaches for online clustering of moving objects location are restricted to instantaneous positions. Subse-quently, they fail to capture the behavior of moving objects over time. By continuously tracking sub-trajectories of moving object at each time window, it becomes possible to gain insight on the current behavior and potentially detect mobility patterns in real time. In our previous work [1], we proposed CUTiS, an incremental algorithm for discovering and maintaining the density-based clusters in trajectory data streams, while tracking the evolution of the clusters. This paper extends [1] to CUTiS* by proposing an indexing structure for sub-trajectory data based on a space-filling curve. The proposed index improves the performance of our approach without losing quality in the clusters results as we show in our experiments conducted on a real dataset.

Several among our daily activities, like traveling to a tourist attraction, are better enjoyed with a group of friends. However, finding the best travel companions is sometimes tricky since we need to form a group of people combining the interest in the proposed destination with the friendship relations among the group members. In this paper we cope with this problem by proposing a new method to recommend the best group of friends with whom to enjoy a specific item, i.e., a travel destination or a venue to visit. Our approach provides a new and original perspective on recommendation: given a user, her social network and a recommended item that is relevant for the user, we want to suggest the best group of friends with whom enjoying the item. This approach differs from traditional group recommendation since it tries to maximize two orthogonal aspects: i) the relevance of the recommended item for every member of the group, and ii), the intra-group social relationships. We introduce the Group Finder framework defining the User-Item Group Formation problem and the possible solutions. We assess our approach in the domain of location recommendation and experiment the proposed solutions using four different publicly available Location Based Social Network (LBSN) datasets. The results achieved confirm the effectiveness and the feasibility of the proposed solutions that outperform strong baselines.

In this paper we propose a new kind of kNN query on time-dependent network, which aims at finding k points of interest that are closest in time to a query point. This query is useful for many kind of applications where a user/customer should ask for a service provided by many moving providers (e.g. Taxi drivers, ambulances, food delivers, etc). We described our solution and present experimental results comparing our proposed algorithm to a baseline approach. The experimental results show that our approach is efficient and effective.

Movement tracking becomes ubiquitous in many applications, which raises great interests in trajectory data analysis and mining. Most existing approaches cluster the whole trajectories offline. This allows characterizing the past movements of the objects but not current patterns. Recent approaches for online clustering of moving objects location are restricted to instantaneous positions. Subsequently, they fail to capture moving objects' behavior over time. By continuously tracking moving objects' sub-trajectories at each time window, rather than just the last position, it becomes possible to gain insight on the current behavior, and potentially detect mobility patterns in real time. In this work, we tackle the problem of discovering and maintaining the density based clusters in trajectory data streams, despite the fact that most moving objects change their position over time. We propose CUTiS, an incremental algorithm to solve this problem, while tracking the evolution of the clusters as well as the membership of the moving objects to the clusters. Our experiments were conducted on real data sets, and it shows the efficiency and the effectiveness of our method.

Trajectory pattern mining allows characterizing movement behavior, which leverages new applications and services. Most existing approaches analyse the whole object trajectory rather that the current movement. Besides existing approaches for online pattern discovery are restricted to instantaneous positions. Subsequently, they fail to capture the movement behaviour along time. By continuously tracking moving objects sub-trajectories at each time window, rather than just the last position, it becomes feasible to gain insight on the current behaviour, and potentially detect mobility patterns in real time. This demonstration presents a novel framework for online mobility pattern discovery in sub-trajectory data streams. Key innovations include: (i) Online discovery of mobility patterns and pattern evolution by tracking the sub-trajectories of moving objects, (ii) A novel structure, called micro-group, to represent the relationship among moving objects, and (iii) An incremental algorithm to maintain micro-groups and to capture their evolution on highly dynamic sub-trajectory data. We present various demonstration scenarios using a real data set.

The increasing availability of positioning data fostered a number of new applications where the knowledge about mobility patterns is essential. However, the research conducted so far on mobility analysis focused on the geometric aspect at the expenses of the semantics of the movement. In this paper, we offer a new vision of semantic trajectory data warehouse that combines the pure geometrical features in terms of temporal and geographical coordinates with more semantic-related contextual information such as the goal of the movement and the transportation means of the moving object, or the activity performed by the moving entity. The conceptual model proposed is called SWOT and is capable of combining these aspects, thus providing a considerable improvement in answering semantic enriched mobility queries.

In this paper we present an algorithm for optimal processing of time-dependent sequenced route queries in road networks, i.e., given a road network where the travel time over an edge is time-dependent and a given ordered list of categories of interest, we find the fastest route between an origin and destination that passes through a sequence of points of interest belonging to each of the specified categories of interest. Our approach uses the A* search paradigm equipped with an admissible heuristic function, thus guaranteed to yield the optimal solution, along with a pruning scheme for further reducing the search space. Our experiments using a real data set have shown our proposed solution to be up to two orders of magnitude faster than a previous solution extended to handle time-dependency.

Graphast is a framework tool that allows developers to compose a number of network models, data importing/exporting services as well as query services, in order to quickly build applications on time-dependent networks. The main goal is to allow developers to implement solutions to different types of problems on time-dependent networks using spatial queries, such as nearest neighbor queries, optimal sequenced routes, etc. Graphast allows the combination of facilities provided by the framework via a public API and/or the building of new facilities, e.g., a new query processing algorithm, and incorporate those into Graphast for others to use them as well. In this paper, we discuss Graphast's architectural components and how one can create/store instances of those components in order to build an application. The steps necessary for building a real world application are also presented.

This work encompasses the development of a new method for classifying tax fraudsters based on fraud indicators. This work was developed in conjunction with a Brazilian fiscal agency aim at avoiding fiscal evasion. The main contribution of this paper is a method that allows classifying and ranking taxpayers analyzing fraud indicators obtained from several fiscal applications. Particularly, we developed a method for identifying frequent fraud patterns using association rules and then we apply two dimension reduction methods (i.e. PCA and SVD) in order to create a fraud scale, which allows ranking taxpayers according to their potential to commit a fraud. Experiments were conducted using real taxpayer data. Tax auditors, specialized in fraud detection, validated our results. Preliminary results show that our method may indicate fraudsters with 80% of accuracy, which is definitely an excellent result.

We consider the problem of finding the k nearest points of interest from a given location in time-dependent road networks, i.e., One where travel time along each edge is a function of the departure time, and where the operating times of the points of interest are also taken into consideration. More specifically, we address the following query: find the k points of interest in which a user can start to be served in the minimum amount of time, accounting for both the travel time to the point of interest and the waiting time, if it is closed. Previous works have proposed solutions to answer kNN queries considering the time dependency of the network but not the operating times of the points of interest. We propose and discuss three solutions to this type of query which are based on the previously proposed incremental network expansion and use the A search algorithm equipped with suitable heuristic functions. We also present experimental results comparing the number of disk access required in each solution with respect to a few different parameters.

In this paper we propose TripBuilder, a new framework for personalized touristic tour planning. We mine from Flickr the information about the actual itineraries followed by a multitude of different tourists, and we match these itineraries on the touristic Point of Interests available from Wikipedia. The task of planning personalized touristic tours is then modeled as an instance of the Generalized Maximum Coverage problem. Wisdom-of-the-crowds information allows us to derive touristic plans that maximize a measure of interest for the tourist given her preferences and visiting time-budget. Experimental results on three different touristic cities show that our approach is effective and outperforms strong baselines.

Positioning systems, combined with inexpensive communication technologies, open interesting possibilities to implement real-time applications that monitor vehicles and support decision making. This paper first discusses basic requirements for proactive real-time monitoring applications. Then, it describes how to structure and geo-reference unstructured text information available on the Internet, with a focus on road conditions change and using available geocoding services. Lastly, the paper outlines an application that monitors a fleet of trucks and incorporates proactive features.

The availability of inexpensive tracking devices, such as GPS-enabled devices, gives the opportunity to collect large amounts of trajectory data from vehicles. In this context, we are interested in the problem of generating the traffic information in time-dependent networks using this kind of data. This problem is not trivial since several works in literature use strong assumptions on the error distribution we want to drop, proposing a gravitational model method to compute road segment average speed from trajectory data. Furthermore we show how to generate travel-time functions from the computed average speeds useful for time-dependent networks routing systems. Our approach allows creating an accurate picture of the traffic conditions in time and space. The method we present in this paper tackles all this aspect showing how its performance over a synthetic dataset and a real case.

Data mashups constructed from independent sources may contain inconsistencies, puzzling the user that observes the data. This paper formalizes the notion of consistent data mashups and introduces a heuristic procedure to compute such mashups.

We analyze urban mobility and public places under a new perspective: how can we feature the places in a city based on how people move among them? To answer this question we need to combine places, like points of interest, with mobility information like the trajectories of individuals moving within a city. To accomplish this, we propose a methodology based on complex network analysis: we build a network of points of interests by connecting places by the individual trajectories passing through them. From such network we compute communities finding groups places highly connected by the mobility of the individuals. We present a case study on real trajectory dataset on the city of Milan, showing a complementary view on the urban mobility that is not covered by the state-of-the art techniques on mobility analysis.

A massive amount of data on moving object trajectories is available today. However, it is still a major challenge to process such information in order to explain moving object interactions, which could help in revealing non-trivial behavioral patterns. To that end, we consider a complex networks-based representation of trajectory data. Frequent encounters among moving objects (trajectory encounters) are used to create the network edges whereas nodes represent trajectories. A real trajectory dataset of vehicles moving within the City of Milan allows us to study the structure of vehicle interactions and validate our method. We create seven networks and compute the clustering coefficient, and the average shortest path length comparing them with those of the Erdős-Rényi model. Our analysis shows that all computed trajectory networks have the small world effect and the scale-free feature similar to the internet and biological networks. Finally, we discuss how these results could be interpreted in the light of the traffic application domain.

Ontologies are being used in order to define common vocabularies to describe the elements of schemas involved in a particular application. The problem of finding correspondences between ontologies concepts, called ontology matching, consists in the discovery of correspondences between terms of vocabularies (represented by ontologies) used by various applications. The majority of solutions proposed in the literature, despite being fully automatic, has heuristic nature and may produce non-satisfactory results. The problem intensifies when dealing with large data sources. The goal of this paper is to propose a method for generation and incremental refinement of correspondences between ontologies. The proposed approach uses filtering techniques, as well as user feedback to support the generation and refinement of such matches. For validation purposes, a tool was developed and some experiments were conducted.

In this paper, we present a three-level ontology-based framework for effectively designing GAV data integration systems. In our approach, the mediated schema is represented by a domain ontology, which provides a conceptual representation of the application. Each local source is described by an application ontology, whose vocabulary is restricted to be a subset of the vocabulary of domain ontology. The three-level architecture permits dividing the mapping definition in two stages: local mappings and mediated mappings. Due to this architecture the problem of query answering can also be broken into two steps. First, the query is decomposed, using the mediated mappings, into a set of elementary sub-queries expressed in terms of the application ontologies. Then, these sub-queries are rewritten, using the local mappings, in terms of their local sources schemas. This paper focus on a method for query processing that addresses the problem of efficient query answering. Our approach is illustrated by an example of a virtual store mediating access to online booksellers.

In-silico experiments require molecular biological knowledge to be mapped into a data model. In a previous work, we have elicited a list of requirements to be attended by a conceptual language for the molecular biology domain. In this paper, we present the evolution of this research by introducing a such language that encompasses most of the identified biological domain requirements. The language offers logical constraints expressions that augments the semantics of data and relationships types, as well as monotonic inheritance. Those aspects address the uncertainty and variability of biological knowledge shortening the gap between scientists mental model and traditional conceptual models.

The collection of moving object data is becoming more and more common, and therefore there is an increasing need for the efficient analysis and knowledge extraction of these data in different application domains. Trajectory data are normally available as sample points, and do not carry semantic information, which is of fundamental importance for the comprehension of these data. Therefore, the analysis of trajectory data becomes expensive from a computational point of view and complex from a user's perspective. Enriching trajectories with semantic geographical information may simplify queries, analysis, and mining of moving object data. In this paper we propose a data preprocessing model to add semantic information to trajectories in order to facilitate trajectory data analysis in different application domains. The model is generic enough to represent the important parts of trajectories that are relevant to the application, not being restricted to one specific application. We present an algorithm to compute the important parts and show that the query complexity for the semantic analysis of trajectories will be significantly reduced with the proposed model.