Data Mining: How Popular Is It?

Data Mining is a process used in the industry, to facilitate decision making. As the name implies, large volumes of data is mined or sifted, to find useful information for decision making. With the advent of E-business, Data Mining has become more important to practitioners. The purpose of this paper is to find out the importance of Data Mining by looking at the different application areas that have used data mining for decision making

Porting Decision Tree Algorithms to Multicore using FastFlow

The whole computer hardware industry embraced multicores. For these machines, the extreme optimisation of sequential algorithms is no longer sufficient to squeeze the real machine power, which can be only exploited via thread-level parallelism. Decision tree algorithms exhibit natural concurrency that makes them suitable to be parallelised. This paper presents an approach for easy-yet-efficient porting of an implementation of the C4.5 algorithm on multicores. The parallel porting requires minimal changes to the original sequential code, and it is able to exploit up to 7X speedup on an Intel dual-quad core machine.Comment: 18 pages + cove

Performance evaluation of a distributed clustering approach for spatial datasets

The analysis of big data requires powerful, scalable, and accurate data analytics techniques that the traditional data mining and machine learning do not have as a whole. Therefore, new data analytics frameworks are needed to deal with the big data challenges such as volumes, velocity, veracity, variety of the data. Distributed data mining constitutes a promising approach for big data sets, as they are usually produced in distributed locations, and processing them on their local sites will reduce significantly the response times, communications, etc. In this paper, we propose to study the performance of a distributed clustering, called Dynamic Distributed Clustering (DDC). DDC has the ability to remotely generate clusters and then aggregate them using an efficient aggregation algorithm. The technique is developed for spatial datasets. We evaluated the DDC using two types of communications (synchronous and asynchronous), and tested using various load distributions. The experimental results show that the approach has super-linear speed-up, scales up very well, and can take advantage of the recent programming models, such as MapReduce model, as its results are not affected by the types of communication

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

Novelty Detection And Cluster Analysis In Time Series Data Using Variational Autoencoder Feature Maps

The identification of atypical events and anomalies in complex data systems is an essential yet challenging task. The dynamic nature of these systems produces huge volumes of data that is often heterogeneous, and the failure to account for this will impede the detection of anomalies. Time series data encompass these issues and its high dimensional nature intensifies these challenges. This research presents a framework for the identification of anomalies in temporal data. A comparative analysis of Centroid, Density and Neural Network-based clustering techniques was performed and their scalability was assessed. This facilitated the development of a new algorithm called the Variational Autoencoder Feature Map (VAEFM) which is an ensemble method that is based on Kohonen’s Self-Organizing Maps (SOM) and Variational Autoencoders. The VAEFM is an unsupervised learning algorithm that models the distribution of temporal data without making a priori assumptions. It incorporates principles of novelty detection to enhance the representational capacity of SOMs neurons, which improves their ability to generalize with novel data. The VAEFM technique was demonstrated on a dataset of accumulated aircraft sensor recordings, to detect atypical events that transpired in the approach phase of flight. This is a proactive means of accident prevention and is therefore advantageous to the Aviation industry. Furthermore, accumulated aircraft data presents big data challenges, which requires scalable analytical solutions. The results indicated that VAEFM successfully identified temporal dependencies in the flight data and produced several clusters and outliers. It analyzed over 2500 flights in under 5 minutes and identified 12 clusters, two of which contained stabilized approaches. The remaining comprised of aborted approaches, excessively high/fast descent patterns and other contributory factors for unstabilized approaches. Outliers were detected which revealed oscillations in aircraft trajectories; some of which would have a lower detection rate using traditional flight safety analytical techniques. The results further indicated that VAEFM facilitates large-scale analysis and its scaling efficiency was demonstrated on a High Performance Computing System, by using an increased number of processors, where it achieved an average speedup of 70%

Finding parallel functional pearls : automatic parallel recursion scheme detection in Haskell functions via anti-unification

This work has been partially supported by the EU H2020 grant “RePhrase: Refactoring Parallel Heterogeneous Resource-Aware Applications–a Software Engineering Approach” (ICT-644235), by COST Action IC1202 (TACLe), supported by COST (European Cooperation in Science and Technology) , by EPSRC grant “Discovery: Pattern Discovery and Program Shaping for Manycore Systems” (EP/P020631/1), and by Scottish Enterprise PS7305CA44.This paper describes a new technique for identifying potentially parallelisable code structures in functional programs. Higher-order functions enable simple and easily understood abstractions that can be used to implement a variety of common recursion schemes, such as maps and folds over traversable data structures. Many of these recursion schemes have natural parallel implementations in the form of algorithmic skeletons. This paper presents a technique that detects instances of potentially parallelisable recursion schemes in Haskell 98 functions. Unusually, we exploit anti-unification to expose these recursion schemes from source-level definitions whose structures match a recursion scheme, but which are not necessarily written directly in terms of maps, folds, etc. This allows us to automatically introduce parallelism, without requiring the programmer to structure their code a priori in terms of specific higher-order functions. We have implemented our approach in the Haskell refactoring tool, HaRe, and demonstrated its use on a range of common benchmarking examples. Using our technique, we show that recursion schemes can be easily detected, that parallel implementations can be easily introduced, and that we can achieve real parallel speedups (up to 23 . 79 × the sequential performance on 28 physical cores, or 32 . 93 × the sequential performance with hyper-threading enabled).PostprintPeer reviewe

Programming Languages for Data-Intensive HPC Applications: a Systematic Mapping Study

A major challenge in modelling and simulation is the need to combine expertise in both software technologies and a given scientific domain. When High-Performance Computing (HPC) is required to solve a scientific problem, software development becomes a problematic issue. Considering the complexity of the software for HPC, it is useful to identify programming languages that can be used to alleviate this issue. Because the existing literature on the topic of HPC is very dispersed, we performed a Systematic Mapping Study (SMS) in the context of the European COST Action cHiPSet. This literature study maps characteristics of various programming languages for data-intensive HPC applications, including category, typical user profiles, effectiveness, and type of articles. We organised the SMS in two phases. In the first phase, relevant articles are identified employing an automated keyword-based search in eight digital libraries. This lead to an initial sample of 420 papers, which was then narrowed down in a second phase by human inspection of article abstracts, titles and keywords to 152 relevant articles published in the period 2006–2018. The analysis of these articles enabled us to identify 26 programming languages referred to in 33 of relevant articles. We compared the outcome of the mapping study with results of our questionnaire-based survey that involved 57 HPC experts. The mapping study and the survey revealed that the desired features of programming languages for data-intensive HPC applications are portability, performance and usability. Furthermore, we observed that the majority of the programming languages used in the context of data-intensive HPC applications are text-based general-purpose programming languages. Typically these have a steep learning curve, which makes them difficult to adopt. We believe that the outcome of this study will inspire future research and development in programming languages for data-intensive HPC applications.Additional co-authors: Sabri Pllana, Ana Respício, José Simão, Luís Veiga, Ari Vis