Analyzing organization e-mails in near real time using hadoop ecosystem tools by Miguel Romero & Alberto de Santos at Big Data Spain 2015

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Analyzing organization e-mails in near real time using Hadoop ecosystem tools. Session presented at Big Data Spain 2015 Conference 15th Oct 2015 Kinépolis Madrid http://www.bigdataspain.org Event promoted by: http://www.paradigmatecnologico.com Abstract: http://www.bigdataspain.org/program/thu/slot-8.html#spch9.3

© Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
Analyzing organization e-mails in Near Real Time using
Hadoop Ecosystem tools
Big Data Spain 2015
Miguel Romero, Hadoop Architect (@donkelito)
Alberto de Santos, Data Scientist (@adesantossierra)
Analytics and Data Management, Enterprise Services

© Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.

© Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
Un trabajador feliz se
ausenta un 28,4% menos
Un trabajador feliz es un
22% más productivo que
uno infeliz
$100,0
00

© Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
Hacerm
e su
amigo
¿Redes
sociales
?
¡Correo
de
empresa!

© Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
To/FromTo/From
AsuntoAsunto
ContenidoContenido
AdjuntosAdjuntos
¿Qué datos tengo?

© Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
¿Qué grupos he encontrado?
Entender quiénes
envían/reciben más emails
Usuarios más influyentes
Detectar grupos de redes y
entender sus
características
Analizar comunidades:
cómo se forman, cómo se
agregan integrantes
Detectar evangelizadores
Detectar expertos
Mezcla de comunidades
Hombres bisagra
Agregación multiescala
Normalized Cuts
Max-flow min-cut algorithm
To/FromTo/From
AsuntoAsunto

© Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
¿De qué hablan en esas comunidades?
To/FromTo/From
AsuntoAsunto

© Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
Online vs offline methods
Detección de
grupos
¿de qué se habla
en cada grupo?
Perfiles más
influyentes
Modelos
clasificación
Detección de
anomalías
Evolución de
conversaciones
Evolución de las
comunidades

© Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
Muchas gracias
Big Data Spain 2015
Miguel Romero (@donkelito)
Alberto de Santos (@adesantossierra)
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DKS EAP (Enterprise Analytical Portal) was first thought of as an integrated analytical portal; a conjunction of BI and BA products (ranging from advanced customer intelligence to marketing analytics) assembled under a unified interface, specifically designed to better assist business in their decision-making. We believe DKS EAP is of special interest for the Big Data community nowadays since it is currently leveraging technologies such as Hadoop, Cassandra, Spark and Storm to improve its analytic capabilities. Accordingly, the talk will briefly present DKS EAP as an integrated Big Data analytic environment, focusing on how the fast-growing needs for real-time, social media and geomarketing data analysis intensified its necessity to embrace Big Data technologies. Session presented at Big Data Spain 2015 Conference 15th Oct 2015 Kinépolis Madrid http://www.bigdataspain.org Event promoted by: http://www.paradigmatecnologico.com Abstract: http://www.bigdataspain.org/program/thu/slot-8.html#spch9.1

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Data Science in 2016: Moving up by Paco Nathan at Big Data Spain 2015

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Stratio presented its open source Lucene-based implementation of Cassandra’s secondary indexes at Cassandra Summit London 2014, which provided several search engine features. It used to be distributed as a fork of Apache Cassandra, which was a huge problem both for users and maintainers. Nowadays, due to some changes introduced at C* 2.1.6, we are proud to announce that it has become a plugin that can be attached to the official Apache Cassandra. With the plugin we have been able to provide C* with geospatial capabilities, making it possible to index geographical positions and perform bounding box and radial distance queries. This is achieved through Lucene’s geospatial module. Session presented at Big Data Spain 2015 Conference 15th Oct 2015 Kinépolis Madrid http://www.bigdataspain.org Event promoted by: http://www.paradigmatecnologico.com Abstract: http://www.bigdataspain.org/program/thu/slot-9.html

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Martyn Jones presented on Big Data, analytics and 4th generation data warehousing. He discussed the importance of a comprehensive data supply framework to obtain, integrate and analyze data from various sources to provide strategic, tactical and operational decision making support. He described moving beyond traditional data warehousing to a 4th generation approach that leverages big data and analytics to gain insights and measure outcomes.

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This document discusses stream processing at scale. It begins with an introduction and agenda. It then discusses scenarios for stream processing like newsfeeds, cybersecurity, and IoT. It presents the canonical stream processing architecture with data buses, real-time and batch processing, and ingestion/serving tiers. The document dives into the essential ingredients for stream processing: scale, reprocessing, accuracy of results, and easy programmability. It provides examples and strategies for each of these essential ingredients to achieve efficient and accurate stream processing at large scales.

IAd-learning: A new e-learning platform by José Antonio Omedes at Big Data Sp...

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Purpose of the talk: Describing the use of Machine Learning and Big Data Techniques to improve the performance of elearning students. Presenting an existing case of an elearning platform (iAdLearn¡ng) and the technology used behind the scenes, to make adaptive/high performance elearning a reality. Session presented at Big Data Spain 2015 Conference 15th Oct 2015 Kinépolis Madrid http://www.bigdataspain.org Event promoted by: http://www.paradigmatecnologico.com Abstract: http://www.bigdataspain.org/program/thu/slot-17.html

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Preprocessing data is one of the most effort consuming tasks in Machine Learning (ML). In the Big Data context, the models automatically derived from data should be as simple as possible, interpretable and fast, and for achieving that we will need to use the best variables, that is, use the best features of such data. Although there are already several libraries available which approach ML tasks in Big Data, that is not the case for FS algorithms yet, and other preprocessing techniques such as discretization. However, the existing FS methods do not scale well when dealing with Big Data. In this presentation, we show our efforts and new ideas for parallelizing standard FS methods for its use on Big Data environments. Session presented at Big Data Spain 2015 Conference 15th Oct 2015 Kinépolis Madrid http://www.bigdataspain.org Event promoted by: http://www.paradigmatecnologico.com Abstract: http://www.bigdataspain.org/program/thu/slot-11.html

Geriatrics Medicine constitutes a clinical research field in which big data, statistical analysis, machine-learning and visualization techniques can provide relevant, solid and lasting benefits, including performance optimization and enhancements in quality of care. Those benefits translate into improvement of patients' quality of life, cost rationalization and better use of resources in the public health system. In this talk, authors will explain how they have used the aforementioned tools with elderly patients' data to realize those benefits. Session presented at Big Data Spain 2015 Conference 15th Oct 2015 Kinépolis Madrid http://www.bigdataspain.org Event promoted by: http://www.paradigmatecnologico.com Abstract: http://www.bigdataspain.org/program/thu/slot-6.html

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Complex machines, e.g. trains or wind turbines, require very solid maintenance procedures. Anticipating the wear of a piece or the failure of a system allows a sensitive maintenance scheduling and prevention of catastrophic failures. The race towards efficiency has enabled the spreading of sensors that collect huge data about the current state of the different components of said machines. Collecting and storing this data can be considered a solvable problem. However, all that data is of no use by itself. An optimal maintenance can derive from decisions that can derive from information that can derive from that big lake of data. Session presented at Big Data Spain 2015 Conference 16th Oct 2015 Kinépolis Madrid http://www.bigdataspain.org Event promoted by: http://www.paradigmatecnologico.com Abstract:http://www.bigdataspain.org/program/fri/slot-36.html#spch36.2

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Euclid is a high-precision survey mission developed in the frame of the Cosmic Vision Program of ESA in order to study the Dark Energy and the Dark Matter. Its Science Ground Segment (SGS) will have to deal with around 175 PB of data both coming from Euclid satellite data, complex pipeline processing, external ground based observations or simulations, and with an output catalog containing the description of around 10 billion of objects with hundreds of attributes. Thus, the implementation of the SGS is a real challenge in terms of architecture and organization. This talk describes the Euclid project challenges, the foreseen architecture, the ongoing proof of concept challenges and the plan for the future. Session presented at Big Data Spain 2015 Conference 15th Oct 2015 Kinépolis Madrid http://www.bigdataspain.org Event promoted by: http://www.paradigmatecnologico.com Abstract: http://www.bigdataspain.org/program/thu/slot-19.html

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Graphs can be built from raw data to discover information by representing relationships between data points as graph connections. Techniques like locality sensitive hashing can be used to efficiently construct graphs from high-dimensional data by mapping similar points to the same "buckets". Once a graph is built, algorithms can find structure like connected components, detect anomalies using local outlier factor, perform clustering, and make inferences about unlabeled nodes. Building graphs is a powerful approach for transforming raw data into useful information through network analysis and machine learning on graphs.

Dataflows: The abstraction that powers Big Data by Raul Castro Fernandez at ...

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http://www.bigdataspain.org/2014/conference/dataflows-the-abstraction-that-powers-the-big-data-technology Dataflows are an omnipresent abstraction across many big data technologies due to its suitability for representing programs in a way that is easy to parallelize. All dataflow models---such as those of Spark or MapReduce---are stateless, which facilitates achieving fault tolerance, a crucial property when running at large-scale. However, this stateless dataflow models have a negative impact on the programming models they expose, which need to adapt to match the stateless nature of the underlying platforms. With the “democratization of data”, different types of users with different skills want answers from their big datasets, but sometimes they lack the skills required to write programs adapted to these specific frameworks: A familiar programming model becomes crucial to open big data value to a broader set of users. Video https://www.youtube.com/watch?v=blY7EleXW6U

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