gradiant - technology offer in business analytics

GALICIAN RESEARCH AND DEVELOPMENT CENTER IN ADVANCED TELECOMMUNICATIONS

Research in Business Analytics

February 2013

Contacts: Arnaud Quirin <[email protected]>

Héctor Cerezo <[email protected]>

CENTRO TECNOLÓXICO DE TELECOMUNICACIÓNS DE GALICIA

Business Analytics in Gradiant: an Overview

Key technologies• Data/Graph mining• Big Data / Business Intelligence • Data network characterization/analysis

HW and SW Resources• HPC resources (304 cores, 1.22 TBs RAM, including 8 virtualization and 2

GPU servers)Project participation• National projects: AIBOT, CELTIC, SmartED• International projects: LIFTGATE

IP & Technology Transfer• Real-time data compression algorithm for in-memory databases (US pat.

pend. 61566689) • Scalable analytical frameworks (in-memory databases, Hadoop,

Cascading, HBase)25+ publications in international journals, conferences, ...


Expertise in Data/Graph Mining

Data/Graph mining• This technology achieves useful insights in large and complex data

collections through the combination of pre-processing, transformation, modeling and analysis techniques.

Application cases in Gradiant• Satellite image classification of marine seaweeds for ecological

conservation, prediction of wind farms electrical supply, prediction and simulation models in emergency departments.

• Discovering the relationships among scientific and industrial data for technological surveys; user profiling, relationship and influence mining using Social Media content.

http://www.mapofscience.com/


Expertise in Scalable Business Intelligence

Big Data/NoSQL• Big Data solutions address the problem of analyzing large datasets with

efficient scalable frameworks. This includes distributed NoSQL databases.

Business Intelligence (BI) • BI solutions address the problem of processing past records of digital

companies to achieve insightful analytics.• Gradiant has experience in main memory analytics, as well as in

technologies to visualize interactive analytics of aggregated business-generated content.

Application cases in Gradiant• Gradiant participates in projects requiring fast-processing of Social

Media content (~4 million records/day). Storage in HBase and analysis with Hadoop and Cascading.

• Gradiant owns in-memory algorithms for analytical databases (US pat. pend. 61566689), comparable to QlikView solutions.


Expertise in Data Network Analysis

Data network characterization/analysis• Data network analytic solutions combining

monitoring and characterization of network operator traffic using simulation and mathematical models.

• Gradiant has experience in projects with Vodafone.

Application cases in Gradiant• Monitoring and traffic characterization of xDSL-

connected users in operator networks, leased link performance analysis and dimensioning.

• Detection of saturation points in leased links using indirect measures.

• Large Network simulation models (experience with ns3 and OMNeT++).

• Visualization of statistical data (d3.js and processing.js).


HW and SW Resources

Hardware• Storage (in cabinet), total 62 TBs

- HP M6612 2TB 6G SAS 7.2K 3.5in MDL HDD x 24- HP M6625 600GB 6G SAS 10K 2.5in HDD x 24

• Servers, total 304 cores and 1.22 TBs RAM- 1 x HP DL980 G7 Intel E7 2860 (80 cores, 512 GBs RAM)- 2 x HP SL390 G Intel X5675 (24 cores, 192 GBs)- 4 x HP DL380G7 Intel Performance (48 cores, 192 GBs)- 2 x HP DL380G7 Intel Efficiency (24 cores, 96 GBs)- 2 x HP DL385G7 AMD Performance (64 cores, 128 GBs)- 2 x HP DL385G7 AMD Efficiency (64 cores, 128GBs)

• Cryptoprocessors- 2 x Utimaco SE10 PCIe boards

Software• Big Data platform

Hadoop/Hbase/Cascading


Current Project Cases (I)

CELTIC: Strategical knowledge gathered from business intelligence technologies• Spanish consortium, supported by

Innterconecta tech funds.• Under contract, 2012-2014• Partners: INDRA (leader), Elogia,

SaecData, Imaxin Software

BA Goals• User profiling for marketing decision

support, design of customer influence metrics relying on scalable efficient frameworks.


Current Project Cases (II)

SmartED: Intelligent management for emergency health services• Spanish consortium, supported by

CDTI tech fund.• Under contract, 2011-2013• Partners: Everis (leader), Arantia,

Balidea

BA Goals• Simulation of hospital emergency

departments, neural net-based prediction models of patient arrivals.


Current Project Cases (III)

AIBOT: Framework for the integration of BSS/OSS systems designed to market non-telecommunication services through telecommunication operators• Spanish consortium, supported by

Innterconecta tech funds• Under contract, 2012-2015• Partners: Sivsa (leader), Discalis,

OptareBA Goals• Root Case Analysis (RCA) algorithms in

the cloud to track incidence causes incomplex telco systems


IP & Technology Transfer

Patent• Real-time data compression algorithm for in-memory

databases (US pat. pend. 61566689)

Non patented IP• Distributed framework to process large amounts of data• Scalable framework for user profiling and relationship

analysis of social media data• Prediction algorithms for hospital emergencies• Indirect techniques to predict saturation in leased

network lines• Root Case Analysis (RCA) prediction algorithms to track

incidences in complex systems


Dissemination

• P. Sendin-Raña, E. Rodriguez-Fernandez, F.J. Gonzalez-Castaño et al, “Web-oriented business intelligence solution based on Associative Query Logic”, Software, Practice and Experience, 40(9): 779-796 (2010).

• P. Sendín Raña, F. J. González Castaño, E. Pérez Barros, P. S. Rodríguez Hernández, F. Gil Castiñeira, J, M. Pousada Carballo, "Improving the performance and functionality of Mondrian open-source OLAP systems," Software, Practice & Experience, 39(3): 279-298 (2009).

• A. Quirin, O. Cordon, V. P. Guerrero-Bote, B. Vargas-Quesada, F. Moya-Anegon; A Quick MST-based Algorithm to Obtain Pathfinder Networks; Journal of the American Society for Information Science and Technology, 59(12): 1912-1924 (2008).

• A. Quirin, O. Cordon, B. Vargas-Quesada, F. de Moya-Anegon; Graph-based Data Mining: A New Tool for the Analysis and Comparison of Scientific Domains Represented as Scientograms; Informetrics, 4(3): 291-312 (2010).

• E. Serrano, A. Quirin, J. Botia, O. Cordon; Debugging Complex Software Systems by Means of Pathfinder Networks; Information Sciences, 180(5): 561-583 (2010).