Abdullah Bawakid

Big Data with Cloud Computing: an insight on the computing environment, MapReduce, and programming frameworks

The term ‘Big Data’ has spread rapidly in the framework of Data Mining and Business Intelligence. This new scenario can be defined by means of those problems that cannot be effectively or efficiently addressed using the standard computing resources that we currently have. We must emphasize that Big Data does not just imply large volumes of data but also the necessity for scalability, i.e., to ensure a response in an acceptable elapsed time. When the scalability term is considered, usually traditional parallel‐type solutions are contemplated, such as the Message Passing Interface or high performance and distributed Database Management Systems. Nowadays there is a new paradigm that has gained popularity over the latter due to the number of benefits it offers. This model is Cloud Computing, and among its main features we has to stress its elasticity in the use of computing resources and space, less management effort, and flexible costs. In this article, we provide an overview on the topic of Big Data, and how the current problem can be addressed from the perspective of Cloud Computing and its programming frameworks. In particular, we focus on those systems for large‐scale analytics based on the MapReduce scheme and Hadoop, its open‐source implementation. We identify several libraries and software projects that have been developed for aiding practitioners to address this new programming model. We also analyze the advantages and disadvantages of MapReduce, in contrast to the classical solutions in this field. Finally, we present a number of programming frameworks that have been proposed as an alternative to MapReduce, developed under the premise of solving the shortcomings of this model in certain scenarios and platforms. WIREs Data Mining Knowl Discov 2014, 4:380–409. doi: 10.1002/widm.1134

A proposal for evolutionary fuzzy systems using feature weighting: dealing with overlapping in imbalanced datasets

In a general scenario of classification, one of the main drawbacks for the achievement of accurate models is the presence of high overlapping among the concepts to be learnt. This drawback becomes more severe when we are addressing problems with an imbalanced class distribution. In such cases, the minority class usually represents the most important target of the classification. The failure to correctly identify the minority class instances is often related to those boundary areas in which they are outnumbered by the majority class examples. Throughout the learning stage of the most common rule learning methodologies, the process is often biased to obtain rules that cover the largest areas of the problem. The reason for this behavior is that these types of algorithms aim to maximize the confidence, measured as a ratio of positive and negative covered examples. Rules that identify small areas, in which minority class examples are poorly represented and overlap with majority class examples, will be discarded in favor of more general rules whose consequent will be unequivocally associated with the majority class. Among all types of rule systems, linguistic Fuzzy Rule Based Systems have shown good behavior in the context of classification with imbalanced datasets. Accordingly, we propose a feature weighting approach which aims at analyzing the significance of the problems variables by weighting the membership degree within the inference process. This is done by applying a different degree of significance to the variables that represent the dataset, enabling to smooth the problem boundaries. These parameters are learnt by means of an optimization process in the framework of evolutionary fuzzy systems. Experimental results using a large number of benchmark problems with different degrees of imbalance and overlapping, show the goodness of our proposal.

Using features extracted from Wikipedia for the task of Word Sense Disambiguation

In this paper, a method using features extracted from Wikipedia for the task of Word Sense Disambiguation (WSD) is presented and evaluated. A term-concepts table constructed from Wikipedia and the redirect links is described. With its help, the Wikipedia internal links along with the categories structure are used to compute the relatedness between any two concepts through a two-level process: a term-concepts expansion followed by a links-based expansion. The result is a ranked list of concepts which are most related to the ambiguous term given the context it exists in. For the evaluation experiment, the benchmark is constructed from a segment of the internal links of Wikipedia. The evaluation results obtained suggest that introducing links analysis and the categories structure to the built term-concepts table provide improvement to the accuracy of the method in the WSD task.

A semantic-based text classification system

This paper presents a system that performs automatic semantic-based text categorization. Using Princeton WordNet, a series of induced methods were implemented that extract semantic features from text and utilize them to decide how similar a document is to different topics. In addition, a bag-of-words method incorporating no knowledge from WordNet is implemented in the system as a basis to compare different WordNet-based approaches. This paper describes the system and reports on a simple analysis performed to evaluate the different implemented methods. At the end, a discussion on the limitations of this study and the future work to optimize the system is presented.

Fuzzy rule based classification systems for big data with MapReduce: granularity analysis

Due to the vast amount of information available nowadays, and the advantages related to the processing of this data, the topics of big data and data science have acquired a great importance in the current research. Big data applications are mainly about scalability, which can be achieved via the MapReduce programming model.It is designed to divide the data into several chunks or groups that are processed in parallel, and whose result is “assembled” to provide a single solution. Among different classification paradigms adapted to this new framework, fuzzy rule based classification systems have shown interesting results with a MapReduce approach for big data. It is well known that the performance of these types of systems has a strong dependence on the selection of a good granularity level for the Data Base. However, in the context of MapReduce this parameter is even harder to determine as it can be also related with the number of Maps chosen for the processing stage. In this paper, we aim at analyzing the interrelation between the number of labels of the fuzzy variables and the scarcity of the data due to the data sampling in MapReduce. Specifically, we consider that as the partitioning of the initial instance set grows, the level of granularity necessary to achieve a good performance also becomes higher. The experimental results, carried out for several Big Data problems, and using the Chi-FRBCS-BigData algorithms, support our claims.

Tracking of EEG Activity Using Motion Estimation to Understand Brain Wiring

The fundamental step in brain research deals with recording electroencephalogram (EEG) signals and then investigating the recorded signals quantitatively. Topographic EEG (visual spatial representation of EEG signal) is commonly referred to as brain topomaps or brain EEG maps. In this chapter, full search full search block motion estimation algorithm has been employed to track the brain activity in brain topomaps to understand the mechanism of brain wiring. The behavior of EEG topomaps is examined throughout a particular brain activation with respect to time. Motion vectors are used to track the brain activation over the scalp during the activation period. Using motion estimation it is possible to track the path from the starting point of activation to the final point of activation. Thus it is possible to track the path of a signal across various lobes.

Centroid-based Classification Enhanced with Wikipedia

Most of the traditional text classification methods employ Bag of Words (BOW) approaches relying on the words frequencies existing within the training corpus and the testing documents. Recently, studies have examined using external knowledge to enrich the text representation of documents. Some have focused on using WordNet which suffers from different limitations including the available number of words, synsets and coverage. Other studies used different aspects of Wikipedia instead. Depending on the features being selected and evaluated and the external knowledge being used, a balance between recall, precision, noise reduction and information loss has to be applied. In this paper, we propose a new Centroid-based classification approach relying on Wikipedia to enrich the representation of documents through the use of Wikpedia’s concepts, categories structure, links, and articles text. We extract candidate concepts for each class with the help of Wikipedia and merge them with important features derived directly from the text documents. Different variations of the system were evaluated and the results show improvements in the performance of the system.

Sentences Simplification for Automatic summarization

In this paper, we emphasize the need for conserving space within sentences by introducing a Sentences Simplification Module (SSM). The module is aimed to shorten the length of sentences via either splitting or compression. We describe how the module is integrated in a Wikipedia-based summarization framework. We highlight the performance differences obtained from introducing such a module by running a series of evaluations.