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Relevant research has been highlighted in the computing community to develop machine learning models capable of predicting the occurrence of crimes, analyzing contexts of crimes, extracting profiles of individuals linked to crime, and analyzing crimes over time. However, models capable of predicting specific crimes, such as homicide, are not commonly found in the current literature. This research presents a machine learning model to predict homicide crimes, using a dataset that uses generic data (without study location dependencies) based on incident report records for 34 different types of crimes, along with time and space data from crime reports. Experimentally, data from the city of Belem - Para, Brazil was used. These data were transformed to make the problem generic, enabling the replication of this model to other locations. In the research, analyses were performed with simple and robust algorithms on the created dataset. With this, statistical tests were performed with 11 different classification methods and the results are related to the prediction’s occurrence and non-occurrence of homicide crimes in the month subsequent to the occurrence of other registered crimes, with 76% assertiveness for both classes of the problem, using Random Forest. Results are considered as a baseline for the proposed problem.

Background Microbes perform a fundamental economic, social, and environmental role in our society. Metagenomics makes it possible to investigate microbes in their natural environments (the complex communities) and their interactions. The way they act is usually estimated by looking at the functions they play in those environments and their responsibility is measured by their genes. The advances of next-generation sequencing technology have facilitated metagenomics research however it also creates a heavy computational burden. Large and complex biological datasets are available as never before. There are many gene predictors available that can aid the gene annotation process though they lack handling appropriately metagenomic data complexities. There is no standard metagenomic benchmark data for gene prediction. Thus, gene predictors may inflate their results by obfuscating low false discovery rates. Results We introduce geneRFinder, an ML-based gene predictor able to outperform state-of-the-art gene prediction tools across this benchmark by using only one pre-trained Random Forest model. Average prediction rates of geneRFinder differed in percentage terms by 54% and 64%, respectively, against Prodigal and FragGeneScan while handling high complexity metagenomes. The specificity rate of geneRFinder had the largest distance against FragGeneScan, 79 percentage points, and 66 more than Prodigal. According to McNemar’s test, all percentual differences between predictors performances are statistically significant for all datasets with a 99% confidence interval. Conclusions We provide geneRFinder, an approach for gene prediction in distinct metagenomic complexities, available at gitlab.com/r.lorenna/generfinder and https://osf.io/w2yd6/, and also we provide a novel, comprehensive benchmark data for gene prediction—which is based on The Critical Assessment of Metagenome Interpretation (CAMI) challenge, and contains labeled data from gene regions—available at https://sourceforge.net/p/generfinder-benchmark.

Genome assembly is one of the most relevant and computationally complex tasks in genomics projects. It aims to reconstruct a genome through the analysis of several small textual fragments of such genome — named reads. Ideally, besides ignoring any errors contained in reads, the reconstructed genome should also optimally combine these reads, thus reaching the original genome. The quality of the genome assembly is relevant because the more reliable the genomes, the more accurate the understanding of the characteristics and functions of living beings, and it allows generating many positive impacts on society, including the prevention and treatment of diseases. The assembly becomes even more complex (and it is termed de novo in this case) when the assembler software is not supplied with a similar genome to be used as a reference. Current assemblers have predominantly used heuristic strategies on computational graphs. Despite being widely used in genomics projects, there is still no irrefutably best assembler for any genome, and the proper choice of these assemblers and their configurations depends on Bioinformatics experts. The use of reinforcement learning has proven to be very promising for solving complex activities without human supervision during their learning process. However, their successful applications are predominantly focused on fictional and entertainment problems-such as games. Based on the above, this work aims to shed light on the application of reinforcement learning to solve this relevant real-world problem, the genome assembly. By expanding the only approach found in the literature that addresses this problem, we carefully explored the aspects of intelligent agent learning, performed by the Q-learning algorithm, to understand its suitability to be applied in scenarios whose characteristics are more similar to those faced by real genome projects. The improvements proposed here include changing the previously proposed reward system and including state space exploration optimization strategies based on dynamic pruning and mutual collaboration with evolutionary computing. These investigations were tried on 23 new environments with larger inputs than those used previously. All these environments are freely available on the internet for the evolution of this research by the scientific community. The results suggest consistent performance progress using the proposed improvements, however, they also demonstrate the limitations of them, especially related to the high dimensionality of state and action spaces. We also present, later, the paths that can be traced to tackle genome assembly efficiently in real scenarios considering recent, successfully reinforcement learning applications — including deep reinforcement learning — from other domains dealing with high-dimensional inputs.

Reinforcement learning has evolved in recent years,and overcoming challenges found in this field. This area, unlikeconventional machine learning, does not learn through a setof observational instances, but through interaction with anenvironment. The sampling efficiency of a reinforcement learningagent is a challenge. That is, how to make an agent learn withinan environment with as little interaction as possible. In this workwe perform an experimental study on the difficulties to integratea strategy of intrinsic motivation to an actor-critic agent toimprove the sampling efficiency. We found results that point to theeffectiveness of the intrinsic motivation as a approach to improvethe agent’s sampling efficiency, as well as its performance. Weshare practical guidelines to assist in the implementation of actor-critic agents to deal with sparse reward environments whilemaking use of intrinsic motivation feedback.

Housekeeping genes (HKGs), are essential for gene expression based studies performed through Reverse Transcriptase-polymerase Chain Reaction (RT-qPCR). These genes are related with the basic cellular processes that are essential for cell maintenance, survival and function. Thus, HKGs should be expressed in all cells of an organism regardless of the tissue type, cell state or cell condition. High-throughput technologies, including RNA sequencing (RNA-seq), are used to study and identify these types of genes. RNA-seq is a high-throughput method that allows the measurement of gene expression profiles in a target tissue or an isolated cell. Moreover, machine learning methods are routinely applied in different genomics related areas to enable the interpretation of large datasets, including those related to gene expression. This study reports a new machine learning based approach to identify candidate HKGs in silico from RNA-seq gene expression data. The approach enabled the identification of stable HKGs candidates in RNA-seq data from Corynebacterium pseudotuberculosis. These genes showed stable expression under different stress conditions as well as low variation index and fold changes. Furthermore, some of these genes were already reported in the literature as HKGs or HKGs candidates for the same or other bacterial organisms, which reinforced the accuracy of the proposed method. We present a novel approach based on K-means algorithm, internal metrics and machine learning methods that can identify stable housekeeping genes from gene expression data with high accuracy and efficiency.

In clustering, one of the most challenging aspects is the validation, whose objective is to evaluate how good a clustering solution is. Sequence binning is a clustering task on metagenomic data analysis. The sequence clustering challenge is essentially putting together sequences belonging to the same genome. As a clustering problem it requires proper use of validation criteria of the discovered partitions. In sequence binning, the concepts of precision and recall, and F-measure index (external validation) are normally used as benchmark. However, on practice, information about the (sub) optimal number of cluster is unknown, so these metrics might be biased to an overestimated “ground truth”. In the case of sequence binning analysis, where the reference information about genomes is not available, how to evaluate the quality of bins resulting from a clustering solution? To answer this question we empirically study both quantitative (internal indexes) and qualitative aspects (biological soundness) while evaluating clustering solutions on the sequence binning problem. Our experimental study indicates that the number of clusters, estimated by binning algorithms, do not have as much impact on the quality of bins by means of biological soundness of the discovered clusters. The quality of the sub-optimal bins (greater than 90%) were identified in both rich and poor clustering partitions. Qualitative validation is essential for proper evaluation of a sequence binning solution, generating bins with sub-optimal quality. Internal indexes can only be used in compliance with qualitative ones as a trade-off between the number of partitions and biological soundness of its respective bins.

Despite the availability of benchmark machine learning (ML) repositories (e.g., UCI, OpenML), there is no standard evaluation strategy yet capable of pointing out which is the best set of datasets to serve as gold standard to test different ML algorithms. In recent studies, Item Response Theory (IRT) has emerged as a new approach to elucidate what should be a good ML benchmark. This work applied IRT to explore the well-known OpenML-CC18 benchmark to identify how suitable it is on the evaluation of classifiers. Several classifiers ranging from classical to ensembles ones were evaluated using IRT models, which could simultaneously estimate dataset difficulty and classifiers’ ability. The Glicko-2 rating system was applied on the top of IRT to summarize the innate ability and aptitude of classifiers. It was observed that not all datasets from OpenML-CC18 are really useful to evaluate classifiers. Most datasets evaluated in this work (84%) contain easy instances in general (e.g., around 10% of difficult instances only). Also, 80% of the instances in half of this benchmark are very discriminating ones, which can be of great use for pairwise algorithm comparison, but not useful to push classifiers abilities. This paper presents this new evaluation methodology based on IRT as well as the tool decodIRT, developed to guide IRT estimation over ML benchmarks.

Reinforcement learning (RL) aims to build intelligent agents able to optimally act after the training process to solve a given goal task in an autonomous and non-deterministic fashion. It has been successfully employed in several areas; however, few RL-based approaches related to genome assembly have been found, especially when considering real input datasets. De novo genome assembly is a crucial step in a number of genome projects, but due to its high complexity, the outcome of state-of-art assemblers is still insufficient to assist researchers in answering all their scientific questions properly. Hence, the development of better assembler is desirable and perhaps necessary, and preliminary studies suggest that RL has the potential to solve this computational task. In this sense, this paper presents an empirical analysis to evaluate this hypothesis, particularly in higher scale, through performance assessment along with time and space complexity analysis of a theoretical approach to the problem of assembly proposed by [2] using the RL algorithm Q-learning. Our analysis shows that, although space and time complexities are limiting scale issues, RL is shown as a viable alternative for solving the DNA fragment assembly problem.

Dimensionality Reduction (DR) is useful to understand high-dimensional data. It attracts wide attention from industry and academia and is employed in areas such as machine learning, data mining, and pattern recognition. This work presents a geometric approach to DR termed Polygonal Coordinate System (PCS), capable of representing multidimensional data in two or three dimensions while preserving their inherent overall structure by taking advantage of a polygonal interface bridging high- and low-dimensional spaces. PCS can handle Big Data by adopting an incremental, geometric DR with linear-time complexity. A new version of t-Distributed Stochastic Neighbor Embedding (t-SNE), a state-of-the-art algorithm for DR, is also provided. It employs a PCS-based deterministic strategy and is named t-Distributed Deterministic Neighbor Embedding (t-DNE). Several synthetic and real data sets were used as well-known real-world problem archetypes in our benchmark, providing a means to evaluate PCS and t-DNE against four embedding-based DR algorithms: two linear-transformation ones (Principal Component Analysis and Non-negative Matrix Factorization) and two nonlinear ones (t-SNE and Sammon’s Mapping). Statistical comparisons of the execution times of these algorithms, by the Friedman’s significance test, highlight the efficiency of PCS in data embedding. PCS tends to surpass its counterparts in several aspects explored in this work, including asymptotic time and space complexity, preservation of global data-inherent structures, number of hyperparameters, and applicability to unobserved data.

Big Data has attracted extensive attention from industry, academia and governments around the world, employing various approaches from many fields such as machine learning, pattern recognition and data visualization. Data visualization is quite useful in the perception of relevant information by a human for gaining understanding and insight from data with high dimensionality. This paper presents a novel approach for dimensionality reduction called Polygonal Coordinate System (PCS), which is able to represent multi-dimensional data into a two-dimensional data. For this purpose, data are represented across a regular polygon or interface between the high dimensions and the 2D plane. PCS can deal with massive data sets by adopting an incremental and efficient dimensionality reduction. Statistical comparison using Spearman's rho correlation highlights the utility of PCS, outperforming the state-of-the-art t-Distributed Stochastic Neighbor Embedding (t-SNE) technique.

Metagenomics is related to the study of microbial genomes, known as metagenomes, describing them through their microorganisms compositions, relationships and activities, thus allowing a greater knowledge about the fundamentals of life and the broad microbial diversity. One way to accomplish such task is by analyzing information from genes contained in metagenomes. The process to identify genes in DNA sequences are usually called gene prediction. This work presents a new gene predictor using the Random Forest classifier. The proposed model obtaining better classification results when compared to state-of-the-art gene prediction tools widely used by the bioinformatics community. Random Forest presented more robust results, being 27% better than Prodigal and 20% better than FragGeneScan w.r.t AUC values while using the independent test set. Feature engineering has been revisited in the gene prediction problem, reinforcing the importance of careful evaluation of assembly a good feature set. K-mer counting features can been seen as the fundamental model building blocks to develop robust gene predictors.

Background Fraudulent milk adulteration is a dangerous practice in the dairy industry that is harmful to consumers since milk is one of the most consumed food products. Milk quality can be assessed by Fourier Transformed Infrared Spectroscopy (FTIR), a simple and fast method for obtaining its compositional information. The spectral data produced by this technique can be explored using machine learning methods, such as neural networks and decision trees, in order to create models that represent the characteristics of pure and adulterated milk samples. Results Thousands of milk samples were collected, some of them were manually adulterated with five different substances and subjected to infrared spectroscopy. This technique produced spectral data from the milk samples composition, which were used for training different machine learning algorithms, such as deep and ensemble decision tree learners. The proposed method is used to predict the presence of adulterants in a binary classification problem and also the specific assessment of which of five adulterants was found through multiclass classification. In deep learning, we propose a Convolutional Neural Network architecture that needs no preprocessing on spectral data. Classifiers evaluated show promising results, with classification accuracies up to 98.76%, outperforming commonly used classical learning methods. Conclusions The proposed methodology uses machine learning techniques on milk spectral data. It is able to predict common adulterations that occur in the dairy industry. Both deep and ensemble tree learners were evaluated considering binary and multiclass classifications and the results were compared. The proposed neural network architecture is able to outperform the composition recognition made by the FTIR equipment and by commonly used methods in the dairy industry. Electronic supplementary material The online version of this article (10.1186/s13040-019-0200-5) contains supplementary material, which is available to authorized users.

Motivation: With the recent advances in DNA sequencing technologies, the study of the genetic composition of living organisms has become more accessible for researchers. Several advances have been achieved because of it, especially in the health sciences. However, many challenges which emerge from the complexity of sequencing projects remain unsolved. Among them is the task of assembling DNA fragments from previously unsequenced organisms, which is classified as an NP-hard (nondeterministic polynomial time hard) problem, for which no efficient computational solution with reasonable execution time exists. However, several tools that produce approximate solutions have been used with results that have facilitated scientific discoveries, although there is ample room for improvement. As with other NP-hard problems, machine learning algorithms have been one of the approaches used in recent years in an attempt to find better solutions to the DNA fragment assembly problem, although still at a low scale. Results: This paper presents a broad review of pioneering literature comprising artificial intelligence-based DNA assemblers—particularly the ones that use machine learning—to provide an overview of state-of-the-art approaches and to serve as a starting point for further study in this field.

Background Taxonomic identification of plants and insects is a hard process that demands expert taxonomists and time, and it’s often difficult to distinguish on morphology only. DNA barcodes allow a rapid species discovery and identification and have been widely used for taxonomic identification by targeting known gene regions that permit to discriminate these species. DNA barcode sequence analysis is usually carried out with processes and tools that still demand a high interaction with the user or researcher. To reduce at most such interaction, we proposed PIPEBAR, a pipeline for DNA chromatograms analysis of Sanger platform sequencing, ensuring high quality consensus sequences along with efficient running time. We also proposed a paired-end reads assembly tool, OverlapPER, which is used in sequence or independently of PIPEBAR. Results PIPEBAR is a command line tool to automatize the processing of large number of trace files. It is accurate as the proprietary Geneious tool and faster than most popular software for barcoding analysis. It is 7 times faster than Geneious and 14 times faster than SeqTrace for processing hundreds of barcoding sequences. OverlapPER is a novel tool for overlapping paired-end reads accurately that accepts both substitution and indel errors and returns both overlapped and non-overlapped regions between a pair of reads. OverlapPER obtained the best results compared to currently used tools when merging 1,000,000 simulated paired-end reads. Conclusions PIPEBAR and OverlapPER run on most operating systems and are freely available, along with supporting code and documentation, at https://sourceforge.net/projects/PIPEBAR/ and https://sourceforge.net/projects/overlapper-reads/. Electronic supplementary material The online version of this article (10.1186/s12859-018-2307-y) contains supplementary material, which is available to authorized users.

Metagenomics can be seeing as a data sampling of microorganisms genome sequences inhabiting a common environment. Those sequences can further be evaluated to estimate the impact of the presence or absence of these microorganism in the environment. Thus, sequence data allows the identification of the taxonomic and functional signatures available in a environmental sample. These signatures are usually not explored in a integrated manner and the lack of proper environmental metadata limit the data analysis. In this paper we propose an integrated methodology to identify and compare metagenomic signatures, correlating them with environmental factors (when they are available). Given that the hierarchical (tree-structured) data solution are kind of a set of nested clusters, a Bubble Tree visualization (by Javascript) is also provided to guide the exploration of the metagenomic signatures. Additionally, it make it practical and an easy-to-use way to compare several metagenomic samples. As far as we are concerned such data and interactive visualization analysis has never been explored before.

The working environment of railways is challenging and complex and often involves high-risk operations. These operations affect both the company staff and inhabitants of the towns and cities alongside the railway lines. To reduce the employees' and public's exposure to risk, railway companies adopt strategies involving trained safety personnel, advanced forms of technology, and special work processes. Nevertheless, unfortunate incidents still occur. To assist railway safety management, researchers developed a visual-analytics system. Using a data analytics workflow, it compiles an incident risk index that processes information about railway incidents. It displays the index on a geographical map, together with socioeconomic information about the associated towns and cities. Feedback on this system suggests that safety engineers and experts can use it to make and communicate decisions.

Transcription is the process of making an RNA copy of a gene sequence. Next, this copy (mRNA) is then translated into proteins. Proteins dictates the expected behavior inside the cells and are required for the structure, function, and regulation of the body's tissues and organs. Together, transcription and translation are known as gene expression. Transcriptograms are basically defined as "images" of gene expression data of genomes, by generating expression profiles for transcriptomes. They allow to assess cell metabolism, being capable of discriminating the stage the cell is going through at a given instant, as well as pointing metabolic changes in altered cellular states as compared to a control state, independently of the transcriptome profilling protocol. Though, they cannot highlight differential expression profiles. We present a new possibility of RNA-Seq data analysis using Transcriptograms for discovering module-based differential expression profiles. We demonstrate its practical application while obtaining more specific gene signatures as well as functional annotations, closely related to biomedical context. Moreover, these signatures are also enriched by survival cancer analysis.

The identification of relevant information in large text databases is a challenging task. One of the reasons is human beings' limitations in handling large volumes of data. A common solution for scavenging data from texts are word clouds. A word cloud illustrates word usage in a document by resizing individual words in documents proportionally to how frequently they appear. Even though word clouds are easy to understand, they are not particularly efficient, because they are static. In addition, the presented information lacks context, i.e., words are not explained and they may lead to radically erroneous interpretations. To tackle these problems we developed VCloud, a tool that allows the user to interact with word clouds, therefore allowing informative and interactive data exploration. Furthermore, our tool also allows one to compare two data sets presented as word clouds. We evaluated VCloud using real data about the evolution of gastritis research through the years. The papers indexed by Pubmed related to this medical context were selected for visualization and data analysis using VCloud. A domain expert explored these visualizations, being able to extract useful information from it. This illustrates how can VCloud be a valuable tool for visual text analytics.

Background High-throughput sequencing technology and bioinformatics have identified chimeric RNAs (chRNAs), raising the possibility of chRNAs expressing particularly in diseases can be used as potential biomarkers in both diagnosis and prognosis. Results The task of discriminating true chRNAs from the false ones poses an interesting Machine Learning (ML) challenge. First of all, the sequencing data may contain false reads due to technical artifacts and during the analysis process, bioinformatics tools may generate false positives due to methodological biases. Moreover, if we succeed to have a proper set of observations (enough sequencing data) about true chRNAs, chances are that the devised model can not be able to generalize beyond it. Like any other machine learning problem, the first big issue is finding the good data to build models. As far as we were concerned, there is no common benchmark data available for chRNAs detection. The definition of a classification baseline is lacking in the related literature too. In this work we are moving towards benchmark data and an evaluation of the fidelity of supervised classifiers in the prediction of chRNAs. Conclusions We proposed a modelization strategy that can be used to increase the tools performances in context of chRNA classification based on a simulated data generator, that permit to continuously integrate new complex chimeric events. The pipeline incorporated a genome mutation process and simulated RNA-seq data. The reads within distinct depth were aligned and analysed by CRAC that integrates genomic location and local coverage, allowing biological predictions at the read scale. Additionally, these reads were functionally annotated and aggregated to form chRNAs events, making it possible to evaluate ML methods (classifiers) performance in both levels of reads and events. Ensemble learning strategies demonstrated to be more robust to this classification problem, providing an average AUC performance of 95 % (ACC=94 %, Kappa=0.87 %). The resulting classification models were also tested on real RNA-seq data from a set of twenty-seven patients with acute myeloid leukemia (AML). Electronic supplementary material The online version of this article (doi:10.1186/s13040-016-0112-6) contains supplementary material, which is available to authorized users.

High-throughput sequencing technology and bioinformatics have identified chimeric RNAs (chRNAs), raising the possibility of chRNAs expressing particularly in diseases can be used as potential biomarkers in both diagnosis and prognosis. The task of discriminating true chRNA from the false ones poses an interesting Machine Learning (ML) challenge. First of all, the sequencing data may contain false reads due to technical artefacts and during the analysis process, bioinformatics tools may generate false positives due to methodological biases. Thus predicting the real signal from the noise can be a hard task. Furthermore, even if we succeed to have a proper set of observations (enough sequencing data) about true chRNAs, chances are that the devised model can not be able to generalize beyond it. Like any other machine learning problem, the first big issue is finding the good data, observations, to build the prediction model. Unfortunately, as far as we were concerned, there is no common benchmark data available for chRNAs. And, the definition of a classification baseline is lacking in the related literature. In this work we are moving towards a benchmark data and a fair comparison analysis unraveling the role of ML techniques in finding chRNAs. We have developed a benchmark pipeline incorporating a mutated genome process and simulated RNA-seq data by Flux Simulator. These sequencing reads were aligned and annotated by CRAC. CRAC offers a new way to analyze the RNA-seq data by integrating genomic location and local coverage, allowing biological predictions in one step. The resulting data were used as a benchmark for our comparison analysis. We have observed that the no free lunch theorem do not hold for ensemble classifiers. Ensemble learning strategies demonstrated to be more robust to this classification problem, providing an average AUC performance of 95% (ACC=94%, Kappa=0.87%).

The catalogs of molecular biology databases does not provide a full description of databases, so the user should select databases using limited information available. Taking into account this fact, in the context of an initiative called BioDBCore, a group of experts proposes core metadata definitions to describe the molecular biology databases. However, how to use these metadata to infer the quality of a database is a clear open issue. In the present work, we propose an ontology-based approach aiming to guide the database selection process from molecular biology database catalogs using these metadata.

Metagenomics is an emerging field in which the power of genome analysis is applied to entire communities of microbes. A large variety of classifiers has been developed for gene prediction though there is lack of an empirical evaluation regarding the core machine learning techniques implemented in these tools. In this work we present an empirical performance evaluation of classification strategies for metagenomic gene prediction. This comparison takes into account distinct supervised learning strategies: one lazy learner, two eager-learners and one ensemble learner. Though the performance of the four base classifiers was good, the ensemble-based strategy with Random Forest has achieved the overall best result.

MicroRNAs are key regulators of eukaryotic gene expression whose fundamental role has already been identified in many cell pathways. The correct identification of miRNAs targets is still a major challenge in bioinformatics and has motivated the development of several computational methods to overcome inherent limitations of experimental analysis. Indeed, the best results reported so far in terms of specificity and sensitivity are associated to machine learning-based methods for microRNA-target prediction. Following this trend, in the current paper we discuss and explore a microRNA-target prediction method based on a random forest classifier, namely RFMirTarget. Despite its well-known robustness regarding general classifying tasks, to the best of our knowledge, random forest have not been deeply explored for the specific context of predicting microRNAs targets. Our framework first analyzes alignments between candidate microRNA-target pairs and extracts a set of structural, thermodynamics, alignment, seed and position-based features, upon which classification is performed. Experiments have shown that RFMirTarget outperforms several well-known classifiers with statistical significance, and that its performance is not impaired by the class imbalance problem or features correlation. Moreover, comparing it against other algorithms for microRNA target prediction using independent test data sets from TarBase and starBase, we observe a very promising performance, with higher sensitivity in relation to other methods. Finally, tests performed with RFMirTarget show the benefits of feature selection even for a classifier with embedded feature importance analysis, and the consistency between relevant features identified and important biological properties for effective microRNA-target gene alignment.

[Excerpt] Following the success of SIGKDD-DDDM2007, ICDM-DDDM2008, ICDM-DDDM2009, and ICDMDDDM2010, the “2011 International Workshop on Domain Driven Data Mining (DDDM2011)” workshop aims to provide a premier forum for sharing findings, knowledge, insight, experience and lessons in tackling potential challenges in discovering actionable knowledge from complex domain problems, promoting interaction and filling the gap between academia and business, and driving a paradigm shift from interesting hidden pattern mining to actionable knowledge discovery in varying data mining domains. [...], (undefined)

Selecting the right data is an essential activity in Genomic-related Information Systems. This work aims to analyze if it is possible to select the best genomic databases from a catalog using information about papers citations related to these genomic databases. The motivation for using information about citations has to do with the fact that it is not easy to obtain proper metadata with respect to these databases. Thus, in this work, information related to papers citations is used for measuring three distinct data quality dimensions: believability, timeliness, and relevancy. Believability is evaluated through the inspection of the number of citations. The variation of the number of citations over time is useful for determining the recency of a database and it is related to the timeliness dimension. Regarding to relevancy, the keywords of papers are useful to indicate the main context of application of these databases.

Business organizations must pay attention to interesting changes in customer behavior in order to anticipate their needs and act accordingly with appropriated business actions. Tracking customer's commercial paths through the products they are interested in is an essential technique to improve business and increase customer satisfaction. Data warehousing (DW) allows us to do so, giving the basic means to record every customer transaction based on the different business strategies established. Although managing such huge amounts of records may imply business advantage, its exploration, especially in a multi-dimensional space (MDS), is a nontrivial task. The more dimensions we want to explore, the more are the computational costs involved in multi-dimensional data analysis (MDA). To make MDA practical in real world business problems, DW researchers have been working on combining data cubing and mining techniques to detect interesting changes in MDS. Such changes can also be detected through gradient queries. While those studies have provided the basis for future research in MDA, just few of them points to preference query selection in MDS. Thus, not only the exploration of changes in MDS is an essential task, but also even more important is ranking most interesting gradients. In this chapter, the authors investigate how to mine and rank the most interesting changes in a MDS applying a TOP-K gradient strategy. Additionally, the authors also propose a gradient-based cubing method to evaluate interesting gradient regions in MDS. So, the challenge is to find maximum gradient regions (MGRs) that maximize the task of raking gradients in a MDS. The authors' evaluation study demonstrates that the proposed method presents a promising strategy for ranking gradients in MDS., (undefined), info:eu-repo/semantics/publishedVersion

In this paper, we present a new OLAP Mining method for exploring interesting trend patterns. Our main goal is to mine the most (TOP-K) significant changes in Multidimensional Spaces (MDS) applying a gradient-based cubing strategy. The challenge is then finding maximum gradient regions, which maximises the task of detecting TOP-K gradient cells. Several heuristics are also introduced to prune MDS efficiently. In this paper, we motivate the importance of the proposed model, and present an efficient and effective method to compute it by: • evaluating significant changes by means of pushing gradient search into the partitioning process • measuring Gradient Regions (GR) spreadness for data cubing • measuring Periodicity Awareness (PA) of a change, assuring that it is a change pattern and not only an isolated event • devising a Rank Gradient-based Cubing to mine significant change patterns in MDS., (undefined), info:eu-repo/semantics/publishedVersion

All over the world we have been assisting to a significant increase of the telecommunication systems usage. People are faced day after day with strong marketing campaigns seeking their attention to new telecommunication products and services. Telecommunication companies struggle in a high competitive business arena. It seems that their efforts were well done, because customers are strongly adopting the new trends and use (and abuse) systematically communication services in their quotidian. Although fraud situations are rare, they are increasing and they correspond to a large amount of money that telecommunication companies lose every year. In this work, we studied the problem of fraud detection in telecommunication systems, especially the cases of superimposed fraud, providing an anomaly detection technique, supported by a signature schema. Our main goal is to detect deviate behaviors in useful time, giving better basis to fraud analysts to be more accurate in their decisions in the establishment of potential fraud situations., Ferreira, P., Alves, R., Belo, O., & Cortesão, L. (2006, 2006//). Establishing Fraud Detection Patterns Based on Signatures. Paper presented at the Advances in Data Mining. Applications in Medicine, Web Mining, Marketing, Image and Signal Mining, Berlin, Heidelberg.

The main idea of iceberg data cubing methods relies on optimization techniques for computing only the cuboids cells above certain minimum support threshold. Even using such approach the curse of dimensionality remains, given the large number of cuboids to compute, which produces, as we know, huge outputs. However, more recently, some efforts have been done on computing only closed cuboids. Nevertheless, for some of the dense databases, which are considered in this paper, even the set of all closed cuboids will be too large. An alternative would be to compute only the maximal cuboids. However, a pure maximal approaching implies loosing some information, this is one can generate the complete set of cuboids cells from its maximal but without their respective aggregation value. To play with some “loss of information” we need to add an interesting measure, that we call the correlated value of a cuboid cell. In this paper, we propose a new notion for reducing cuboids aggregation by means of computing only the maximal-correlated cuboids cells, and present the M3C-Cubing algorithm that brings out those cuboids. Our evaluation study shows that the method followed is a promising candidate for scalable data cubing, reducing the number of cuboids by at least an order of magnitude or more in comparison with that of closed ones.

Most of the itemsetmining approaches are memory-like and run outside of the database. On the other hand, when we deal with data warehouse the size of tables is extremely huge for memory copy. In addition, using a pure SQL-like approach is quite inefficient. Actually, those implementations rarely take advantages of database programming. Furthermore, RDBMS vendors offer a lot of features for taking control and management of the data. We purpose a pattern growth mining approach by means of database programming for finding allfrequent itemsets. The main idea is to avoid one-at-a-time record retrieval from the database, saving both the copying and process context switching, expensive joins, and table reconstruction. The empirical evaluation of our approach shows that runs competitively with the most known itemset mining implementations based on SQL. Our performance evaluation was made with SQL Server 2000 (v.8) and T-SQL, throughout several synthetical datasets.

Several business applications such as marketing basket analysis, clickstream analysis, fraud detection and churning migration analysis demand gradient data analysis. By employing gradient data analysis one is able to identify trends, outliers and answering "what-if' questions over large databases. Gradient queries were first introduced by Imielinski et al [1] as the cubegrade problem. The main idea is to detect interesting changes in a multidimensional space (MDS). Thus, changes in a set of measures (aggregates) are associated with changes in sector characteristics (dimensions). MDS contains a huge number of cells which poses great challenge for mining gradient cells on a useful time. Dong et al [2] have proposed gradient constraints to smooth the computational costs involved in such queries. Even by using such constraints on large databases, the number of interesting cases to evaluate is still large. In this work, we are interested to explore best cases (Top-K cells) of interesting multidimensional gradients. There several studies on Top-K queries, but preference queries with multidimensional selection were introduced quite recently by Dong et al [9]. Furthermore, traditional Top-K methods work well in presence of convex functions (gradients are non-convex ones). We have revisited iceberg cubing for complex measures, since it is the basis for mining gradient cells. We also propose a gradient-based cubing strategy to evaluate interesting gradient regions in MDS. Thus, the main challenge is to find maximum gradient regions (MGRs) that maximize the task of mining Top-K gradient cells. Our performance study indicates that our strategy is effective on finding the most interesting gradients in multidimensional space., Supported by a Ph.D. Scholarship from FCT-Foundation of Science and Technology, Ministry of Science of Portugal