Découverte d'évènements par contenu visuel dans les médias sociaux

The ease of publishing content on social media sites brings to the Web an ever increasing amount of user generated content captured during, and associated with, real life events. Social media documents shared by users often reflect their personal experience of the event. Hence, an event can be seen as a set of personal and local views, recorded by different users. These event records are likely to exhibit similar facets of the event but also specific aspects. By linking different records of the same event occurrence we can enable rich search and browsing of social media events content. Specifically, linking all the occurrences of the same event would provide a general overview of the event. In this dissertation we present a content-based approach for leveraging the wealth of social media documents available on the Web for event identification and characterization. To match event occurrences in social media, we develop a new visual-based method for retrieving events in huge photocollections, typically in the context of User Generated Content. The main contributions of the thesis are the following : (1) a new visual-based method for retrieving events in photo collections, (2) a scalable and distributed framework for Nearest Neighbors Graph construction for high dimensional data, (3) a collaborative content-based filtering technique for selecting relevant social media documents for a given event.L’évolution du web, de ce qui était typiquement connu comme un moyen de communication à sens unique en mode conversationnel, a radicalement changé notre manière de traiter l’information. Des sites de médias sociaux tels que Flickr et Facebook, offrent des espaces d’échange et de diffusion de l’information. Une information de plus en plus riche, mais aussi personnelle, et qui s’organise, le plus souvent, autour d’événements de la vie réelle. Ainsi, un événement peut être perçu comme un ensemble de vues personnelles et locales, capturées par différents utilisateurs. Identifier ces différentes instances permettrait, dès lors, de reconstituer une vue globale de l’événement. Plus particulièrement, lier différentes instances d’un même événement profiterait à bon nombre d’applications tel que la recherche, la navigation ou encore le filtrage et la suggestion de contenus. L’objectif principal de cette thèse est l’identification du contenu multimédia, associé à un événement dans de grandes collections d’images. Une première contribution est une méthode de recherche d’événements basée sur le contenu visuel. La deuxième contribution est une approche scalable et distribuée pour la construction de graphes des K plus proches voisins. La troisième contribution est une méthode collaborative pour la sélection de contenu pertinent. Plus particulièrement, nous nous intéresserons aux problèmes de génération automatique de résumés d’événements et suggestion de contenus dans les médias sociaux

Scaling kNN queries using statistical learning

The k-Nearest Neighbour (kNN) method is a fundamental building block for many sophisticated statistical learning models and has a wide application in different fields; for instance, in kNN regression, kNN classification, multi-dimensional items search, location-based services, spatial analytics, etc. However, nowadays with the unprecedented spread of data generated by computing and communicating devices has resulted in a plethora of low-dimensional large-scale datasets and their users' community, the need for efficient and scalable kNN processing is pressing. To this end, several parallel and distributed approaches and methodologies for processing exact kNN in low-dimensional large-scale datasets have been proposed; for example Hadoop-MapReduce-based kNN query processing approaches such as Spatial-Hadoop (SHadoop), and Spark-based approaches like Simba. This thesis contributes with a variety of methodologies for kNN query processing based on statistical and machine learning techniques over large-scale datasets. This study investigates the exact kNN query performance behaviour of the well-known Big Data Systems, SHadoop and Simba, that proposes building multi-dimensional Global and Local Indexes over low dimensional large-scale datasets. The rationale behind such methods is that when executing exact kNN query, the Global and Local indexes access a small subset of a large-scale dataset stored in a distributed file system. The Global Index is used to prune out irrelevant subsets of the dataset; while the multiple distributed Local Indexes are used to prune out unnecessary data elements of a partition (subset). The kNN execution algorithm of SHadoop and Simba involves loading data elements that reside in the relevant partitions from disks/network points to memory. This leads to significantly high kNN query response times; so, such methods are not suitable for low-latency applications and services. An extensive literature review showed that not enough attention has been given to access relatively small-sized but relevant data using kNN query only. Based on this limitation, departing from the traditional kNN query processing methods, this thesis contributes two novel solutions: Coordinator With Index (COWI) and Coordinator with No Index(CONI) approaches. The essence of both approaches rests on adopting a coordinator-based distributed processing algorithm and a way to structure computation and index the stored datasets that ensures that only a very small number of pieces of data are retrieved from the underlying data centres, communicated over the network, and processed by the coordinator for every kNN query. The expected outcome is that scalability is ensured and kNN queries can be processed in just tens of milliseconds. Both approaches are implemented using a NoSQL Database (HBase) achieving up to three orders of magnitude of performance gain compared with state of the art methods -SHadoop and Simba. It is common practice that the current state-of-the-art approaches for exact kNN query processing in low-dimensional space use Tree-based multi-dimensional Indexing methods to prune out irrelevant data during query processing. However, as data sizes continue to increase, (nowadays it is not uncommon to reach several Petabytes), the storage cost of Tree-based Index methods becomes exceptionally high, especially when opted to partition a dataset into smaller chunks. In this context, this thesis contributes with a novel perspective on how to organise low-dimensional large-scale datasets based on data space transformations deriving a Space Transformation Organisation Structure (STOS). STOS facilitates kNN query processing as if underlying datasets were uniformly distributed in the space. Such an approach bears significant advantages: first, STOS enjoys a minute memory footprint that is many orders of magnitude smaller than Index-based approaches found in the literature. Second, the required memory for such meta-data information over large-scale datasets, unlike related work, increases very slowly with dataset size. Hence, STOS enjoys significantly higher scalability. Third, STOS is relatively efficient to compute, outperforming traditional multivariate Index building times, and comparable, if not better, query response times. In the literature, the exact kNN query in a large-scale dataset was limited to low-dimensional space; this is because the query response time and memory space requirement of the Tree-based index methods increase with dimension. Unable to solve such exponential dependency on the dimension, researchers assume that no efficient solution exists and propose approximation kNN in high dimensional space. Unlike the approximated kNN query that tries to retrieve approximated nearest neighbours from large-scale datasets, in this thesis a new type of kNN query referred to as ‘estimated kNN query’ is proposed. The estimated kNN query processing methodology attempts to estimate the nearest neighbours based on the marginal cumulative distribution of underlying data using statistical copulas. This thesis showcases the performance trade-off of exact kNN and the estimate kNN queries in terms of estimation error and scalability. In contrast, kNN regression predicts that a value of a target variable based on kNN; but, particularly in a high dimensional large-scale dataset, a query response time of kNN regression, can be a significantly high due to the curse of dimensionality. In an effort to tackle this issue, a new probabilistic kNN regression method is proposed. The proposed method statistically predicts the values of a target variable of kNN without computing distance. In different contexts, a kNN as missing value algorithm in high dimensional space in Pytha, a distributed/parallel missing value imputation framework, is investigated. In Pythia, a different way of indexing a high-dimensional large-scale dataset is proposed by the group (not the work of the author of this thesis); by using such indexing methods, scaling-out of kNN in high dimensional space was ensured. Pythia uses Adaptive Resonance Theory (ART) -a machine learning clustering algorithm- for building a data digest (aka signatures) of large-scale datasets distributed across several data machines. The major idea is that given an input vector, Pythia predicts the most relevant data centres to get involved in processing, for example, kNN. Pythia does not retrieve exact kNN. To this end, instead of accessing the entire dataset that resides in a data-node, in this thesis, accessing only relevant clusters that reside in appropriate data-nodes is proposed. As we shall see later, such method has comparable accuracy to that of the original design of Pythia but has lower imputation time. Moreover, the imputation time does not significantly grow with a size of a dataset that resides in a data node or with the number of data nodes in Pythia. Furthermore, as Pythia depends utterly on the data digest built by ART to predict relevant data centres, in this thesis, the performance of Pythia is investigated by comparing different signatures constructed by a different clustering algorithms, the Self-Organising Maps. In this thesis, the performance advantages of the proposed approaches via extensive experimentation with multi-dimensional real and synthetic datasets of different sizes and context are substantiated and quantified

Scalable big data systems: Architectures and optimizations

Big data analytics has become not just a popular buzzword but also a strategic direction in information technology for many enterprises and government organizations. Even though many new computing and storage systems have been developed for big data analytics, scalable big data processing has become more and more challenging as a result of the huge and rapidly growing size of real-world data. Dedicated to the development of architectures and optimization techniques for scaling big data processing systems, especially in the era of cloud computing, this dissertation makes three unique contributions. First, it introduces a suite of graph partitioning algorithms that can run much faster than existing data distribution methods and inherently scale to the growth of big data. The main idea of these approaches is to partition a big graph by preserving the core computational data structure as much as possible to maximize intra-server computation and minimize inter-server communication. In addition, it proposes a distributed iterative graph computation framework that effectively utilizes secondary storage to maximize access locality and speed up distributed iterative graph computations. The framework not only considerably reduces memory requirements for iterative graph algorithms but also significantly improves the performance of iterative graph computations. Last but not the least, it establishes a suite of optimization techniques for scalable spatial data processing along with three orthogonal dimensions: (i) scalable processing of spatial alarms for mobile users traveling on road networks, (ii) scalable location tagging for improving the quality of Twitter data analytics and prediction accuracy, and (iii) lightweight spatial indexing for enhancing the performance of big spatial data queries.Ph.D

Distributed multi-label learning on Apache Spark

This thesis proposes a series of multi-label learning algorithms for classification and feature selection implemented on the Apache Spark distributed computing model. Five approaches for determining the optimal architecture to speed up multi-label learning methods are presented. These approaches range from local parallelization using threads to distributed computing using independent or shared memory spaces. It is shown that the optimal approach performs hundreds of times faster than the baseline method. Three distributed multi-label k nearest neighbors methods built on top of the Spark architecture are proposed: an exact iterative method that computes pair-wise distances, an approximate tree-based method that indexes the instances across multiple nodes, and an approximate local sensitive hashing method that builds multiple hash tables to index the data. The results indicated that the predictions of the tree-based method are on par with those of an exact method while reducing the execution times in all the scenarios. The aforementioned method is then used to evaluate the quality of a selected feature subset. The optimal adaptation for a multi-label feature selection criterion is discussed and two distributed feature selection methods for multi-label problems are proposed: a method that selects the feature subset that maximizes the Euclidean norm of individual information measures, and a method that selects the subset of features maximizing the geometric mean. The results indicate that each method excels in different scenarios depending on type of features and the number of labels. Rigorous experimental studies and statistical analyses over many multi-label metrics and datasets confirm that the proposals achieve better performances and provide better scalability to bigger data than the methods compared in the state of the art

Layered performance modelling and evaluation for cloud topic detection and tracking based big data applications

“Big Data” best characterized by its three features namely “Variety”, “Volume” and “Velocity” is revolutionizing nearly every aspect of our lives ranging from enterprises to consumers, from science to government. A fourth characteristic namely “value” is delivered via the use of smart data analytics over Big Data. One such Big Data Analytics application considered in this thesis is Topic Detection and Tracking (TDT). The characteristics of Big Data brings with it unprecedented challenges such as too large for traditional devices to process and store (volume), too fast for traditional methods to scale (velocity), and heterogeneous data (variety). In recent times, cloud computing has emerged as a practical and technical solution for processing big data. However, while deploying Big data analytics applications such as TDT in cloud (called cloud-based TDT), the challenge is to cost-effectively orchestrate and provision Cloud resources to meet performance Service Level Agreements (SLAs). Although there exist limited work on performance modeling of cloud-based TDT applications none of these methods can be directly applied to guarantee the performance SLA of cloud-based TDT applications. For instance, current literature lacks a systematic, reliable and accurate methodology to measure, predict and finally guarantee performances of TDT applications. Furthermore, existing performance models fail to consider the end-to-end complexity of TDT applications and focus only on the individual processing components (e.g. map reduce). To tackle this challenge, in this thesis, we develop a layered performance model of cloud-based TDT applications that take into account big data characteristics, the data and event flow across myriad cloud software and hardware resources and diverse SLA considerations. In particular, we propose and develop models to capture in detail with great accuracy, the factors having a pivotal role in performances of cloud-based TDT applications and identify ways in which these factors affect the performance and determine the dependencies between the factors. Further, we have developed models to predict the performance of cloud-based TDT applications under uncertainty conditions imposed by Big Data characteristics. The model developed in this thesis is aimed to be generic allowing its application to other cloud-based data analytics applications. We have demonstrated the feasibility, efficiency, validity and prediction accuracy of the proposed models via experimental evaluations using a real-world Flu detection use-case on Apache Hadoop Map Reduce, HDFS and Mahout Frameworks

Health systems data interoperability and implementation

Objective The objective of this study was to use machine learning and health standards to address the problem of clinical data interoperability across healthcare institutions. Addressing this problem has the potential to make clinical data comparable, searchable and exchangeable between healthcare providers. Data sources Structured and unstructured data has been used to conduct the experiments in this study. The data was collected from two disparate data sources namely MIMIC-III and NHanes. The MIMIC-III database stored data from two electronic health record systems which are CareVue and MetaVision. The data stored in these systems was not recorded with the same standards; therefore, it was not comparable because some values were conflicting, while one system would store an abbreviation of a clinical concept, the other would store the full concept name and some of the attributes contained missing information. These few issues that have been identified make this form of data a good candidate for this study. From the identified data sources, laboratory, physical examination, vital signs, and behavioural data were used for this study. Methods This research employed a CRISP-DM framework as a guideline for all the stages of data mining. Two sets of classification experiments were conducted, one for the classification of structured data, and the other for unstructured data. For the first experiment, Edit distance, TFIDF and JaroWinkler were used to calculate the similarity weights between two datasets, one coded with the LOINC terminology standard and another not coded. Similar sets of data were classified as matches while dissimilar sets were classified as non-matching. Then soundex indexing method was used to reduce the number of potential comparisons. Thereafter, three classification algorithms were trained and tested, and the performance of each was evaluated through the ROC curve. Alternatively the second experiment was aimed at extracting patient’s smoking status information from a clinical corpus. A sequence-oriented classification algorithm called CRF was used for learning related concepts from the given clinical corpus. Hence, word embedding, random indexing, and word shape features were used for understanding the meaning in the corpus. Results Having optimized all the model’s parameters through the v-fold cross validation on a sampled training set of structured data ( ), out of 24 features, only ( 8) were selected for a classification task. RapidMiner was used to train and test all the classification algorithms. On the final run of classification process, the last contenders were SVM and the decision tree classifier. SVM yielded an accuracy of 92.5% when the and parameters were set to and . These results were obtained after more relevant features were identified, having observed that the classifiers were biased on the initial data. On the other side, unstructured data was annotated via the UIMA Ruta scripting language, then trained through the CRFSuite which comes with the CLAMP toolkit. The CRF classifier obtained an F-measure of 94.8% for “nonsmoker” class, 83.0% for “currentsmoker”, and 65.7% for “pastsmoker”. It was observed that as more relevant data was added, the performance of the classifier improved. The results show that there is a need for the use of FHIR resources for exchanging clinical data between healthcare institutions. FHIR is free, it uses: profiles to extend coding standards; RESTFul API to exchange messages; and JSON, XML and turtle for representing messages. Data could be stored as JSON format on a NoSQL database such as CouchDB, which makes it available for further post extraction exploration. Conclusion This study has provided a method for learning a clinical coding standard by a computer algorithm, then applying that learned standard to unstandardized data so that unstandardized data could be easily exchangeable, comparable and searchable and ultimately achieve data interoperability. Even though this study was applied on a limited scale, in future, the study would explore the standardization of patient’s long-lived data from multiple sources using the SHARPn open-sourced tools and data scaling platformsInformation ScienceM. Sc. (Computing

Breaking Computational Barriers to Perform Time Series Pattern Mining at Scale and at the Edge

Uncovering repeated behavior in time series is an important problem in many domains such as medicine, geophysics, meteorology, and many more. With the continuing surge of smart/embedded devices generating time series data, there is an ever growing need to perform analysis on datasets of increasing size. Additionally, there is an increasing need for analysis at low power edge devices due to latency problems inherent to the speed of light and the sheer amount of data being recorded. The matrix profile has proven to be a tool highly suitable for pattern mining in time series; however, a naive approach to computing the matrix profile makes it impossible to use effectively in both the cloud and at the edge. This dissertation shows how, through the use of GPUs and machine learning, the matrix profile is computed more feasibly, both at cloud-scale and at sensor-scale. In addition, it illustrates why both of these types of computation are important and what new insights they can provide to practitioners working with time series data