Distributed top-k aggregation queries at large

Top-k query processing is a fundamental building block for efficient ranking in a large number of applications. Efficiency is a central issue, especially for distributed settings, when the data is spread across different nodes in a network. This paper introduces novel optimization methods for top-k aggregation queries in such distributed environments. The optimizations can be applied to all algorithms that fall into the frameworks of the prior TPUT and KLEE methods. The optimizations address three degrees of freedom: 1) hierarchically grouping input lists into top-k operator trees and optimizing the tree structure, 2) computing data-adaptive scan depths for different input sources, and 3) data-adaptive sampling of a small subset of input sources in scenarios with hundreds or thousands of query-relevant network nodes. All optimizations are based on a statistical cost model that utilizes local synopses, e.g., in the form of histograms, efficiently computed convolutions, and estimators based on order statistics. The paper presents comprehensive experiments, with three different real-life datasets and using the ns-2 network simulator for a packet-level simulation of a large Internet-style network

Data-driven design of intelligent wireless networks: an overview and tutorial

Data science or "data-driven research" is a research approach that uses real-life data to gain insight about the behavior of systems. It enables the analysis of small, simple as well as large and more complex systems in order to assess whether they function according to the intended design and as seen in simulation. Data science approaches have been successfully applied to analyze networked interactions in several research areas such as large-scale social networks, advanced business and healthcare processes. Wireless networks can exhibit unpredictable interactions between algorithms from multiple protocol layers, interactions between multiple devices, and hardware specific influences. These interactions can lead to a difference between real-world functioning and design time functioning. Data science methods can help to detect the actual behavior and possibly help to correct it. Data science is increasingly used in wireless research. To support data-driven research in wireless networks, this paper illustrates the step-by-step methodology that has to be applied to extract knowledge from raw data traces. To this end, the paper (i) clarifies when, why and how to use data science in wireless network research; (ii) provides a generic framework for applying data science in wireless networks; (iii) gives an overview of existing research papers that utilized data science approaches in wireless networks; (iv) illustrates the overall knowledge discovery process through an extensive example in which device types are identified based on their traffic patterns; (v) provides the reader the necessary datasets and scripts to go through the tutorial steps themselves

Fundamentals of Large Sensor Networks: Connectivity, Capacity, Clocks and Computation

Sensor networks potentially feature large numbers of nodes that can sense their environment over time, communicate with each other over a wireless network, and process information. They differ from data networks in that the network as a whole may be designed for a specific application. We study the theoretical foundations of such large scale sensor networks, addressing four fundamental issues- connectivity, capacity, clocks and function computation. To begin with, a sensor network must be connected so that information can indeed be exchanged between nodes. The connectivity graph of an ad-hoc network is modeled as a random graph and the critical range for asymptotic connectivity is determined, as well as the critical number of neighbors that a node needs to connect to. Next, given connectivity, we address the issue of how much data can be transported over the sensor network. We present fundamental bounds on capacity under several models, as well as architectural implications for how wireless communication should be organized. Temporal information is important both for the applications of sensor networks as well as their operation.We present fundamental bounds on the synchronizability of clocks in networks, and also present and analyze algorithms for clock synchronization. Finally we turn to the issue of gathering relevant information, that sensor networks are designed to do. One needs to study optimal strategies for in-network aggregation of data, in order to reliably compute a composite function of sensor measurements, as well as the complexity of doing so. We address the issue of how such computation can be performed efficiently in a sensor network and the algorithms for doing so, for some classes of functions.Comment: 10 pages, 3 figures, Submitted to the Proceedings of the IEE

Study on Different Topology Manipulation Algorithms in Wireless Sensor Network

Wireless sensor network (WSN) comprises of spatially distributed autonomous sensors to screen physical or environmental conditions and to agreeably go their information through the network to a principle area. One of the critical necessities of a WSN is the efficiency of vitality, which expands the life time of the network. At the same time there are some different variables like Load Balancing, congestion control, coverage, Energy Efficiency, mobility and so on. A few methods have been proposed via scientists to accomplish these objectives that can help in giving a decent topology control. In the piece, a few systems which are accessible by utilizing improvement and transformative strategies that give a multi target arrangement are examined. In this paper, we compare different algorithms' execution in view of a few parameters intended for every target and the outcomes are analyzed. DOI: 10.17762/ijritcc2321-8169.15029

ROOT - A C++ Framework for Petabyte Data Storage, Statistical Analysis and Visualization

ROOT is an object-oriented C++ framework conceived in the high-energy physics (HEP) community, designed for storing and analyzing petabytes of data in an efficient way. Any instance of a C++ class can be stored into a ROOT file in a machine-independent compressed binary format. In ROOT the TTree object container is optimized for statistical data analysis over very large data sets by using vertical data storage techniques. These containers can span a large number of files on local disks, the web, or a number of different shared file systems. In order to analyze this data, the user can chose out of a wide set of mathematical and statistical functions, including linear algebra classes, numerical algorithms such as integration and minimization, and various methods for performing regression analysis (fitting). In particular, ROOT offers packages for complex data modeling and fitting, as well as multivariate classification based on machine learning techniques. A central piece in these analysis tools are the histogram classes which provide binning of one- and multi-dimensional data. Results can be saved in high-quality graphical formats like Postscript and PDF or in bitmap formats like JPG or GIF. The result can also be stored into ROOT macros that allow a full recreation and rework of the graphics. Users typically create their analysis macros step by step, making use of the interactive C++ interpreter CINT, while running over small data samples. Once the development is finished, they can run these macros at full compiled speed over large data sets, using on-the-fly compilation, or by creating a stand-alone batch program. Finally, if processing farms are available, the user can reduce the execution time of intrinsically parallel tasks - e.g. data mining in HEP - by using PROOF, which will take care of optimally distributing the work over the available resources in a transparent way

On the role of pre and post-processing in environmental data mining

The quality of discovered knowledge is highly depending on data quality. Unfortunately real data use to contain noise, uncertainty, errors, redundancies or even irrelevant information. The more complex is the reality to be analyzed, the higher the risk of getting low quality data. Knowledge Discovery from Databases (KDD) offers a global framework to prepare data in the right form to perform correct analyses. On the other hand, the quality of decisions taken upon KDD results, depend not only on the quality of the results themselves, but on the capacity of the system to communicate those results in an understandable form. Environmental systems are particularly complex and environmental users particularly require clarity in their results. In this paper some details about how this can be achieved are provided. The role of the pre and post processing in the whole process of Knowledge Discovery in environmental systems is discussed

Scalable processing of aggregate functions for data streams in resource-constrained environments

The fast evolution of data analytics platforms has resulted in an increasing demand for real-time data stream processing. From Internet of Things applications to the monitoring of telemetry generated in large datacenters, a common demand for currently emerging scenarios is the need to process vast amounts of data with low latencies, generally performing the analysis process as close to the data source as possible. Devices and sensors generate streams of data across a diversity of locations and protocols. That data usually reaches a central platform that is used to store and process the streams. Processing can be done in real time, with transformations and enrichment happening on-the-fly, but it can also happen after data is stored and organized in repositories. In the former case, stream processing technologies are required to operate on the data; in the latter batch analytics and queries are of common use. Stream processing platforms are required to be malleable and absorb spikes generated by fluctuations of data generation rates. Data is usually produced as time series that have to be aggregated using multiple operators, being sliding windows one of the most common abstractions used to process data in real-time. To satisfy the above-mentioned demands, efficient stream processing techniques that aggregate data with minimal computational cost need to be developed. However, data analytics might require to aggregate extensive windows of data. Approximate computing has been a central paradigm for decades in data analytics in order to improve the performance and reduce the needed resources, such as memory, computation time, bandwidth or energy. In exchange for these improvements, the aggregated results suffer from a level of inaccuracy that in some cases can be predicted and constrained. This doctoral thesis aims to demonstrate that it is possible to have constant-time and memory efficient aggregation functions with approximate computing mechanisms for constrained environments. In order to achieve this goal, the work has been structured in three research challenges. First we introduce a runtime to dynamically construct data stream processing topologies based on user-supplied code. These dynamic topologies are built on-the-fly using a data subscription model de¿ned by the applications that consume data. The subscription-based programing model enables multiple users to deploy their own data-processing services. On top of this runtime, we present the Amortized Monoid Tree Aggregator general sliding window aggregation framework, which seamlessly combines the following features: amortized O(1) time complexity and a worst-case of O(log n) between insertions; it provides both a window aggregation mechanism and a window slide policy that are user programmable; the enforcement of the window sliding policy exhibits amortized O(1) computational cost for single evictions and supports bulk evictions with cost O(log n); and it requires a local memory space of O(log n). The framework can compute aggregations over multiple data dimensions, and has been designed to support decoupling computation and data storage through the use of distributed Key-Value Stores to keep window elements and partial aggregations. Specially motivated by edge computing scenarios, we contribute Approximate and Amortized Monoid Tree Aggregator (A2MTA). It is, to our knowledge, the first general purpose sliding window programable framework that combines constant-time aggregations with error bounded approximate computing techniques. A2MTA uses statistical analysis of the stream data in order to perform inaccurate aggregations, providing a critical reduction of needed resources for massive stream data aggregation, and an improvement of performance.La ràpida evolució de les plataformes d'anàlisi de dades ha resultat en un increment de la demanda de processament de fluxos continus de dades en temps real. Des de la internet de les coses fins al monitoratge de telemetria generada en grans servidors, una demanda recurrent per escenaris emergents es la necessitat de processar grans quantitats de dades amb latències molt baixes, generalment fent el processat de les dades tant a prop dels origines com sigui possible. Les dades son generades com a fluxos continus per dispositius que utilitzen una varietat de localitzacions i protocols. Aquests processat de les dades s pot fer en temps real amb les transformacions efectuant-se al vol, i en aquest cas la utilització de plataformes de processat d'streams és necessària. Les plataformes de processat d'streams cal que absorbeixin pics de freqüència de dades. Les dades es generen com a series temporals que s'agreguen fent servir multiples operadors, on les finestres són l'abstracció més habitual. Per a satisfer les baixes latències i maleabilitat requerides, els operadors necesiten tenir un cost computacional mínim, inclús amb extenses finestres de dades per a agregar. La computació aproximada ha sigut durant decades un paradigma rellevant per l'anàlisi de dades on cal millorar el rendiment de diferents algorismes i reduir-ne el temps de computació, la memòria requerida, l'ample de banda o el consum energètic. A canvi d'aquestes millores, els resultats poden patir d'una falta d'exactitud que pot ser estimada i controlada. Aquesta tesi doctoral vol demostrar que es posible tenir funcions d'agregació pel processat d'streams que tinc un cost de temps constant, sigui eficient en termes de memoria i faci ús de computació aproximada. Per aconseguir aquests objectius, aquesta tesi està dividida en tres reptes. Primer presentem un entorn per a la construcció dinàmica de topologies de computació d'streams de dades utilitzant codi d'usuari. Aquestes topologies es construeixen fent servir un model de subscripció a streams, en el que les aplicación consumidores de dades amplien les topologies mentre s'estan executant. Aquest entorn permet multiples entitats ampliant una mateixa topologia. A sobre d'aquest entorn, presentem un framework de propòsit general per a l'agregació de finestres de dades anomenat AMTA (Amortized Monoid Tree Aggregator). Aquest framework combina: temps amortitzat constant per a totes les operacions, amb un cas pitjor logarítmic; programable tant en termes d'agregació com en termes d'expulsió d'elements de la finestra. L'expulsió massiva d'elements de la finestra es considera una operació atòmica, amb un cost amortitzat constant; i requereix espai en memoria local per a O(log n) elements de la finestra. Aquest framework pot computar agregacions sobre multiples dimensions de dades, i ha estat dissenyat per desacoplar la computació de les dades del seu desat, podent tenir els continguts de la finestra distribuits en diferents màquines. Motivats per la computació en l'edge (edge computing), hem contribuit A2MTA (Approximate and Amortized Monoid Tree Aggregator). Des de el nostre coneixement, es el primer framework de propòsit general per a la computació de finestres que combina un cost constant per a totes les seves operacions amb tècniques de computació aproximada amb control de l'error. A2MTA fa us d'anàlisis estadístics per a poder fer agregacions amb error limitat, reduint críticament els recursos necessaris per a la computació de grans quantitats de dades

Constant-time approximate sliding window framework with error control

Stream Processing is a crucial element for the Edge Computing paradigm, in which large amount of devices generate data at the edge of the network. This data needs to be aggregated and processed on-the-move across different layers before reaching the Cloud. Therefore, defining Stream Processing services that adapt to different levels of resource availability is of paramount importance. In this context, Stream Processing frameworks need to combine efficient algorithms with low computational complexity to manage sliding windows, with the ability to adjust resource demands for different deployment scenarios, from very low capacity edge devices to virtually unlimited Cloud platforms. The Approximate Computing paradigm provides improved performance and adaptive resource demands in data analytics, at the price of introducing some level of inaccuracy that can be calculated. In this paper we present the Approximate and Amortized Monoid Tree Aggregator (A 2 MTA). It is, to our knowledge, the first general purpose sliding window programable framework that combines constant-time aggregations with error bounded approximate computing techniques. It is very suitable for adverse stream processing environments, such as resource scarce multi-tenant edge computing. The framework can compute aggregations over multiple data dimensions, setting error bounds on any of them, and has been designed to support decoupling computation and data storage through the use of distributed Key-Value Stores to keep window elements and partial aggregations.This project is partially supported by the European Research Council (ERC), Spain under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 639595). It is also partially supported by the Ministry of Economy of Spain under contract TIN2015-65316-P and Generalitat de Catalunya, Spain under contract 2014SGR1051, by the ICREA Academia program, and by the BSC-CNS Severo Ochoa program (SEV-2015-0493).Peer ReviewedPostprint (author's final draft