Scaling DBSCAN-like algorithms for event detection systems in Twitter

The increasing use of mobile social networks has lately transformed news media. Real-world events are nowadays reported in social networks much faster than in traditional channels. As a result, the autonomous detection of events from networks like Twitter has gained lot of interest in both research and media groups. DBSCAN-like algorithms constitute a well-known clustering approach to retrospective event detection. However, scaling such algorithms to geographically large regions and temporarily long periods present two major shortcomings. First, detecting real-world events from the vast amount of tweets cannot be performed anymore in a single machine. Second, the tweeting activity varies a lot within these broad space-time regions limiting the use of global parameters. Against this background, we propose to scale DBSCAN-like event detection techniques by parallelizing and distributing them through a novel density-aware MapReduce scheme. The proposed scheme partitions tweet data as per its spatial and temporal features and tailors local DBSCAN parameters to local tweet densities. We implement the scheme in Apache Spark and evaluate its performance in a dataset composed of geo-located tweets in the Iberian peninsula during the course of several football matches. The results pointed out to the benefits of our proposal against other state-of-the-art techniques in terms of speed-up and detection accuracy.Peer ReviewedPostprint (author's final draft

Event detection in location-based social networks

With the advent of social networks and the rise of mobile technologies, users have become ubiquitous sensors capable of monitoring various real-world events in a crowd-sourced manner. Location-based social networks have proven to be faster than traditional media channels in reporting and geo-locating breaking news, i.e. Osama Bin Laden’s death was first confirmed on Twitter even before the announcement from the communication department at the White House. However, the deluge of user-generated data on these networks requires intelligent systems capable of identifying and characterizing such events in a comprehensive manner. The data mining community coined the term, event detection , to refer to the task of uncovering emerging patterns in data streams . Nonetheless, most data mining techniques do not reproduce the underlying data generation process, hampering to self-adapt in fast-changing scenarios. Because of this, we propose a probabilistic machine learning approach to event detection which explicitly models the data generation process and enables reasoning about the discovered events. With the aim to set forth the differences between both approaches, we present two techniques for the problem of event detection in Twitter : a data mining technique called Tweet-SCAN and a machine learning technique called Warble. We assess and compare both techniques in a dataset of tweets geo-located in the city of Barcelona during its annual festivities. Last but not least, we present the algorithmic changes and data processing frameworks to scale up the proposed techniques to big data workloads.This work is partially supported by Obra Social “la Caixa”, by the Spanish Ministry of Science and Innovation under contract (TIN2015-65316), by the Severo Ochoa Program (SEV2015-0493), by SGR programs of the Catalan Government (2014-SGR-1051, 2014-SGR-118), Collectiveware (TIN2015-66863-C2-1-R) and BSC/UPC NVIDIA GPU Center of Excellence.We would also like to thank the reviewers for their constructive feedback.Peer ReviewedPostprint (author's final draft

Theoretically-Efficient and Practical Parallel DBSCAN

The DBSCAN method for spatial clustering has received significant attention due to its applicability in a variety of data analysis tasks. There are fast sequential algorithms for DBSCAN in Euclidean space that take $O(n\log n)$ work for two dimensions, sub-quadratic work for three or more dimensions, and can be computed approximately in linear work for any constant number of dimensions. However, existing parallel DBSCAN algorithms require quadratic work in the worst case, making them inefficient for large datasets. This paper bridges the gap between theory and practice of parallel DBSCAN by presenting new parallel algorithms for Euclidean exact DBSCAN and approximate DBSCAN that match the work bounds of their sequential counterparts, and are highly parallel (polylogarithmic depth). We present implementations of our algorithms along with optimizations that improve their practical performance. We perform a comprehensive experimental evaluation of our algorithms on a variety of datasets and parameter settings. Our experiments on a 36-core machine with hyper-threading show that we outperform existing parallel DBSCAN implementations by up to several orders of magnitude, and achieve speedups by up to 33x over the best sequential algorithms

Classification algorithms for Big Data with applications in the urban security domain

A classification algorithm is a versatile tool, that can serve as a predictor for the future or as an analytical tool to understand the past. Several obstacles prevent classification from scaling to a large Volume, Velocity, Variety or Value. The aim of this thesis is to scale distributed classification algorithms beyond current limits, assess the state-of-practice of Big Data machine learning frameworks and validate the effectiveness of a data science process in improving urban safety. We found in massive datasets with a number of large-domain categorical features a difficult challenge for existing classification algorithms. We propose associative classification as a possible answer, and develop several novel techniques to distribute the training of an associative classifier among parallel workers and improve the final quality of the model. The experiments, run on a real large-scale dataset with more than 4 billion records, confirmed the quality of the approach. To assess the state-of-practice of Big Data machine learning frameworks and streamline the process of integration and fine-tuning of the building blocks, we developed a generic, self-tuning tool to extract knowledge from network traffic measurements. The result is a system that offers human-readable models of the data with minimal user intervention, validated by experiments on large collections of real-world passive network measurements. A good portion of this dissertation is dedicated to the study of a data science process to improve urban safety. First, we shed some light on the feasibility of a system to monitor social messages from a city for emergency relief. We then propose a methodology to mine temporal patterns in social issues, like crimes. Finally, we propose a system to integrate the findings of Data Science on the citizenry’s perception of safety and communicate its results to decision makers in a timely manner. We applied and tested the system in a real Smart City scenario, set in Turin, Italy

Empirical Evaluation Of Parallelizing Correlation Algorithms For Sequential Telecommunication Devices Data

Context: Connected devices within IoT is a source of generating big data. The data measured from devices consists of large number of features from hundreds to thousands. Analyzing these features is both data and computing intensive. Distributed and parallel processing frameworks such as Apache Spark provide in-memory processing technologies to design feature analytic workflows. However, algorithms for discovering data patterns and trends over time series are not necessarily ready to cooperate issues such as data partition, data shuffling that rise from distribution and parallelism. Aim: This thesis aims to explore the relation between algorithm characteristics and parallelisms as well as the effects on clustering results and the system performance. Method: System level techniques were developed to address particularly the data partition, load-balancing and data shuffling issues. Furthermore, these techniques are applied to adopt clustering algorithms on distributed parallel computing frameworks. In the evaluation, two workflows were built in which each consists of a clustering algorithm and its corresponding metrics for measuring distances of any two time series data. Result: These system level techniques improve the overall performance and execution of the workflows. Conclusion: The distribution and parallel workflows address both algorithmic factors and parallelism factors to improve accuracy and performance of processing big time series data of connected devices

LENTA: Longitudinal Exploration for Network Traffic Analysis from Passive Data

In this work, we present LENTA (Longitudinal Exploration for Network Traffic Analysis), a system that supports the network analysts in the identification of traffic generated by services and applications running on the web. In the case of URLs observed in operative network, LENTA simplifies the analyst’s job by letting her observe few hundreds of clusters instead of the original hundred thousands of single URLs. We implement a self-learning methodology, where the system grows its knowledge, which is used in turn to automatically associate traffic to previously observed services, and identify new traffic generated by possibly suspicious applications. This approach lets the analysts easily observe changes in network traffic, identify new services, and unexpected activities. We follow a data-driven approach and run LENTA on traces collected both in ISP networks and directly on hosts via proxies. We analyze traffic in batches of 24-hours worth of traffic. Big data solutions are used to enable horizontal scalability and meet performance requirements. We show that LENTA allows the analyst to clearly understand which services are running on their network, possibly highlighting malicious traffic and changes over time, greatly simplifying the view and understanding of the network traffic

Distributed mining of convoys in large scale datasets

Tremendous increase in the use of the mobile devices equipped with the GPS and other location sensors has resulted in the generation of a huge amount of movement data. In recent years, mining this data to understand the collective mobility behavior of humans, animals and other objects has become popular. Numerous mobility patterns, or their mining algorithms have been proposed, each representing a specific movement behavior. Convoy pattern is one such pattern which can be used to find groups of people moving together in public transport or to prevent traffic jams. A convoy is a set of at least m objects moving together for at least k consecutive time stamps where m and k are user-defined parameters. Existing algorithms for detecting convoy patterns do not scale to real-life dataset sizes. Therefore in this paper, we propose a generic distributed convoy pattern mining algorithm called DCM and show how such an algorithm can be implemented using the MapReduce framework. We present a cost model for DCM and a detailed theoretical analysis backed by experimental results. We show the effect of partition size on the performance of DCM. The results from our experiments on different data-sets and hardware setups, show that our distributed algorithm is scalable in terms of data size and number of nodes, and more efficient than any existing sequential as well as distributed convoy pattern mining algorithm, showing speed-ups of up to 16 times over SPARE, the state of the art distributed co-movement pattern mining framework. DCM is thus able to process large datasets which SPARE is unable to.SCOPUS: ar.jDecretOANoAutActifinfo:eu-repo/semantics/publishe

Quality of Service Aware Data Stream Processing for Highly Dynamic and Scalable Applications

Huge amounts of georeferenced data streams are arriving daily to data stream management systems that are deployed for serving highly scalable and dynamic applications. There are innumerable ways at which those loads can be exploited to gain deep insights in various domains. Decision makers require an interactive visualization of such data in the form of maps and dashboards for decision making and strategic planning. Data streams normally exhibit fluctuation and oscillation in arrival rates and skewness. Those are the two predominant factors that greatly impact the overall quality of service. This requires data stream management systems to be attuned to those factors in addition to the spatial shape of the data that may exaggerate the negative impact of those factors. Current systems do not natively support services with quality guarantees for dynamic scenarios, leaving the handling of those logistics to the user which is challenging and cumbersome. Three workloads are predominant for any data stream, batch processing, scalable storage and stream processing. In this thesis, we have designed a quality of service aware system, SpatialDSMS, that constitutes several subsystems that are covering those loads and any mixed load that results from intermixing them. Most importantly, we natively have incorporated quality of service optimizations for processing avalanches of geo-referenced data streams in highly dynamic application scenarios. This has been achieved transparently on top of the codebases of emerging de facto standard best-in-class representatives, thus relieving the overburdened shoulders of the users in the presentation layer from having to reason about those services. Instead, users express their queries with quality goals and our system optimizers compiles that down into query plans with an embedded quality guarantee and leaves logistic handling to the underlying layers. We have developed standard compliant prototypes for all the subsystems that constitutes SpatialDSMS

DENCAST: distributed density-based clustering for multi-target regression

Recent developments in sensor networks and mobile computing led to a huge increase in data generated that need to be processed and analyzed efficiently. In this context, many distributed data mining algorithms have recently been proposed. Following this line of research, we propose the DENCAST system, a novel distributed algorithm implemented in Apache Spark, which performs density-based clustering and exploits the identified clusters to solve both single- and multi-target regression tasks (and thus, solves complex tasks such as time series prediction). Contrary to existing distributed methods, DENCAST does not require a final merging step (usually performed on a single machine) and is able to handle large-scale, high-dimensional data by taking advantage of locality sensitive hashing. Experiments show that DENCAST performs clustering more efficiently than a state-of-the-art distributed clustering algorithm, especially when the number of objects increases significantly. The quality of the extracted clusters is confirmed by the predictive capabilities of DENCAST on several datasets: It is able to significantly outperform (p-value $$<0.05$$ ) state-of-the-art distributed regression methods, in both single and multi-target settings

Extracting Maritime Traffic Networks from AIS Data Using Evolutionary Algorithm

The presented method reconstructs a network (a graph) from AIS data, which reflects vessel traffic and can be used for route planning. The approach consists of three main steps: maneuvering points detection, waypoints discovery, and edge construction. The maneuvering points detection uses the CUSUM method and reduces the amount of data for further processing. The genetic algorithm with spatial partitioning is used for waypoints discovery. Finally, edges connecting these waypoints form the final maritime traffic network. The approach aims at advancing the practice of maritime voyage planning, which is typically done manually by a ship’s navigation officer. The authors demonstrate the results of the implementation using Apache Spark, a popular distributed and parallel computing framework. The method is evaluated by comparing the results with an on-line voyage planning application. The evaluation shows that the approach has the capacity to generate a graph which resembles the real-world maritime traffic network