Transcript
Data Sheet
IBM Software
Highlights
• Analytic accelerators provide advanced
analytics for various data types
• Application accelerators address specific
big data use cases
• Included with IBM Big Data Platform
components IBM InfoSphere BigInsights
and IBM InfoSphere Streams, at no
additional charge
IBM accelerators
for big data
Speeding the development and implementation of big
data solutions
With information streaming in from more sources than ever before—
social media, outdoor sensors, cameras, videos, check-out transactions
and so on—organizations face the daunting challenge of gaining insights
from new data sources and types. Many business leaders know that
big data is an important resource for creating new opportunities and
becoming more agile. However, wanting to get started and knowing how
to start are two different notions.
Building applications for handling big data typically requires a certain
skill set: understanding numerous data sources and types, familiarity with
particular programming languages, knowledge of advanced analytics and
more. Unfortunately, companies often lack the time or money to invest in
building these areas of expertise. Enter IBM® accelerators for big data.
IBM accelerators for big data are packaged software components
that speed the development and implementation of specifc big data
solutions. They are included with two components of the IBM Big
Data Platform—IBM InfoSphere® BigInsights™ and IBM InfoSphere
Streams—at no additional cost. The accelerators offer organizations
business logic, data processing and UI/visualization capabilities that
can be tailored for a given use case or industry need. By leveraging
IBM experience and information management best practices, they help
eliminate the complexity of building big data applications and reduce the
time-to-value for big data deployments.
IBM provides two types of accelerators for big data (see Figure 1):
1. Analytic accelerators address specifc data types or operations with
advanced analytics, such as text analytics and geospatial data.
2. Application accelerators address specifc use cases, such as log
analysis and social media insights, and include both industry-specifc
and cross-industry features.
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Solutions
IBM Big Data Platform
Analytic accelerators
• Text
• Geospatial
• Time series
• Data mining
Application accelerators
• Finance
• Machine data
• Social data
• Telco event data
Accelerators
Visualization and discovery Application development Systems management
Hadoop System Stream computing Data warehouse
Information integration and governance
Big data infrastructure
Analytics and decision management
Analytic accelerators
The IBM portfolio of analytic accelerators for big data includes
capabilities for text, geospatial and time series analysis, as well
as data mining.
Text analytics: Gain broad insights with highly
accurate analysis of textual context
The IBM text analytics engine is a powerful information
extraction system, designed for use in big data and
MapReduce parallel processing environments with
exceptional performance. Developed by IBM Research,
this analytics engine can identify meaning within text using
technology similar to that demonstrated in IBM Watson™
for natural language processing. It includes more than 100
pre-built rules (called annotators) that understand textual
meaning. For example, the text analytics accelerator can
differentiate between a frst and last name, or indicate whether
an address is for a street or apartment. The annotators are
context-sensitive and can determine the relationship between
terms even if they are separated by other text.
Case in point: Text analytics
In the healthcare industry, text analytics can pull information
out of patient notes taken during routine clinical rounds,
understand the context and detect meaning. This leads to
more accurate records and analysis, better diagnoses and
improved patient care.
Figure 1: IBM accelerators for big data and the IBM Big Data Platform.
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Geospatial analytics: Do more with location data
The geospatial accelerator calculates distances and directions
between points on the globe using fat earth, spherical and
spheroid models. Key functions of this accelerator include:
• Real-time processing of location data
• Effcient search for entities in or around an area of interest
• GPS location data mapped to line—for example, road
network segments
• Calculators to determine the spatial relationship between
different features on the Earth
Case in point: Geospatial analytics
In an urban environment, geospatial analytics can improve
the efficiency of public transportation by enabling city bus
systems to detect traffic patterns, calculate the fastest
routes and optimize directions in real time.
Time series analytics: Predict the future
The time series accelerator facilitates real-time predictive
analysis of regularly generated data, including:
• Physiology/nature data: Audio, ECG, weather data and
seismic data
• Systems data: Response times and performance measures
• Market/fnance data: Stocks, sales and other metrics
• Sensor data: Smart meters, temperature sensors, pressure
sensors and water salinity sensors
The time series accelerator also includes digital fltering
capabilities to remove noise or unwanted artifacts, smooth
out data and decompose in low-varying and high-varying
patterns. It can perform pattern and correlation analysis to
identify patterns in the data and expose correlations in various
data streams, as well as decomposition to estimate spectral
components of the data stream and transform and project them
into new data representations. To help “predict the future,” the
accelerator includes forecasting capabilities to detect general
trends and estimate the value of future data streams before they
arrive (also known as look-ahead stream processing).
Case in point: Time series analytics
Energy utilities depend on data to measure demand for
resources, manage distribution and react to outages or other
disruptions. Utilities can use the time series accelerator to
analyze huge volumes of environmental measurements to
detect anomalies in weather patterns, or tap into constant
streams of data from smart meters to help predict energy
usage based on price and temperature.
Data mining: Score data against predictive models
The IBM accelerator for data mining provides a set of IBM
Streams Processing Language (SPL) operators for scoring real-
time data against predictive models represented in Predictive
Model Markup Language (PMML). The PMML standard is
supported by multiple analytics platforms such as IBM SPSS®,
SAS and open-source R. Analytics operators provided by this
accelerator include:
• Classifcation: Decision trees, Naïve Bayes and
logistic regression
• Clustering: Demographic clustering and Kohonen clustering
• Regression: Linear regression, polynomial regression and
transform regression
• Associations: Association rules
With this accelerator, operators can take a PMML fle
describing a predictive model as an input stream and update
the model dynamically for rapid insight. For seamless
integration, this accelerator embeds libraries directly from
IBM InfoSphere Warehouse.
Case in point: Data mining
A bank can use the data mining accelerator to detect fraud
in real time, based on analysis of historic records that
include fraudulent purchases. With the insights gleaned,
banks can greatly improve their decision making and
analysis of customer purchase patterns over time for better
retention and relationships.
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Application accelerators
The IBM portfolio of application accelerators for big data
includes capabilities for fnance, machine data, social data and
telecommunications (telco) event data analytics.
Finance analytics: Incorporate critical real-time
insights for financial markets
The fnance analytics accelerator provides automated options
and equity trading analytics including:
• Real-time market data ingestion and management
• Real-time decision support for equities, derivatives,
commodity and foreign exchange (Forex) trading
• Ability to incorporate additional contextual awareness (news,
weather and so on) into trading decisions
• Real-time, cross-asset pricing and real-time, continuous
enterprise risk-level monitoring and liquidity management
across trading desks and geographies
• Continuous real-time trade monitoring to identify
fraudulent trading
• SPL building blocks to develop InfoSphere Streams–based
fnancial applications
• Out-of-the-box adapters and operators that save
organizations time by implementing functions that don’t
need to be built from scratch:
– Out-of-the-box adapters:
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Financial Information Exchange (FIX)
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IBM WebSphere® Front Office (WFO)
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Low-latency messaging for InfiniBand support
– Operators:
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European style for options (11 methods)
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American style for options (11 methods)
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Greeks (for example, Delta and Theta)
Case in point: Finance analytics
A financial services company can take the out-of-the box
capabilities of the finance analytics accelerator, including
sample trading options algorithms, and plug them into its
own decision-making algorithms. This helps facilitate
automated options trading.
Machine data analytics: Create new possibilities for
operational data applications
The IBM accelerator for machine data analytics ingests and
processes large volumes of machine data to provide in-depth
business insights. Machine data comprises information that was
automatically created by a computer process, application or other
device; sources include machines ranging from IT equipment
and sensors to meters and network devices. The accelerator
provides a range of data-intensive capabilities, including:
• Data ingestion, metadata validation and writing to Apache
Hadoop Distributed File System (HDFS)
• Data parsing and extraction, available out of the box and as
extendable rules
• Data indexing for complete re-indexing (ingest new batches)
or batch-incremental indexing updates (update already
indexed batches) using InfoSphere BigIndex, a component of
InfoSphere BigInsights that delivers low-latency, full-text
search capabilities for big data, with user-confgurable felds
and fact hierarchies
• Confgurable faceted search leveraging Lucene wildcard
syntax support for text search
• Data transformation to link records and create sessions based
on time windows, a set of “seed” records and transitive linkage
• Statistical modeling to model patterns and perform
correlation analysis
Case in point: Machine data analytics
In the energy and utilities industry, data streams in from the field
24x7—distribution grid monitors, sensors on drilling platforms,
pipeline gauges, local and regional transmission stations
and so on. The IBM accelerator for machine data analytics
can ingest data from multiple equipment sources; analyze,
index and parse it; and turn it into accessible insights that
help companies manage peak demand, address constrained
resources and improve system reliability and efficiency.
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Social data analytics: Get closer to your customers
The IBM accelerator for social data analytics is designed to
process large volumes of social media data to provide additional
views into customer-facing activities such as retention efforts,
lead generation, brand management and marketing campaigns.
Highly extendable and customizable, this accelerator supports:
• Data ingestion from sources such as Gnip and Boardreader
• Processing of both streaming data and data at rest
• Micro-segmentation attributes, such as personal information
(gender, location, parental status, marital status, employment
and so on) as well as interests and products owned or
previously purchased
• Entity resolution across different social media sources
• Monitoring of outputs and measures such as buzz, sentiment,
intent to buy or start service and intent to attend or see, so
organizations can then take specifc and necessary actions
• Visualization using IBM BigSheets, a browser-based,
spreadsheet-like tool that comes with InfoSphere BigInsights
and allows users to explore data stored in InfoSphere
BigInsights applications and create analytic queries without
writing any code
Case in point: Social data analytics
If a movie studio wants to study the effectiveness of
trailers for the latest film release, the social data analytics
accelerator can help process real-time feedback culled
from millions of social media profiles as the trailer airs.
Telco event data analytics: Enhance customer
profiles with streaming data
The telco event data analytics accelerator provides the ability
to ingest, process and analyze large volumes of streaming data
from telecommunications systems in real time. It can take in
different types and volumes of streaming data, and then analyze
subscriber information and call data records (CDRs) to support
billing mediation. CDRs can be in ASN.1 (Abstract Syntax
Notation.1), ASCII or binary format. Once the data is ingested,
the accelerator can perform rule-based data transformations,
including lookups against tables, computation of key felds
and generation of unique CDR IDs. Registration and error-
handling capabilities extend to fle-level duplicate detection
and error-checking of individual CDRs. Information can be
output to different locations, including flesystems, databases
and parallel databases. CDR deduplication is performed using
a Bloom flter with a variable window of up to 15 days. The
accelerator also features KPI computation, which aggregates
user and cell information, offers summary statistics and
facilitates end-to-end consistency, as well as dynamic table and
rule updates.
This level of insight enables telecommunications companies
to offer targeted, differentiated services for a high-quality
customer experience, which helps to strengthen customer
loyalty and reduce churn. Features such as personalized
billing and real-time fraud detection also help increase
operational effciency.
Case in point: Telco event data analytics
One telecommunications company uses the IBM accelerator
to access real-time analyses of 7 billion CDRs a day, reducing
its data processing time from 12 hours to 1 minute and
cutting hardware costs to one-eighth of the original amount.
Getting started with accelerators
IBM accelerators for big data are designed to help
organizations address big data challenges and leverage
industry-leading expertise without having to redesign
existing systems and processes. As part of the IBM Big Data
Platform, the accelerators help organizations integrate and
manage the full variety, velocity and volume of data; apply
advanced analytics to information in its native form; visualize
all available data for ad hoc analysis; and build new analytic
applications based on workload optimization and scheduling.
The IBM Big Data Platform
The IBM Big Data Platform is a comprehensive collection
of best-of-breed technologies and services that help
organizations integrate data from disparate sources,
analyze big data in real time, anticipate future outcomes
and rapidly generate insights for capitalizing on new
opportunities. In addition to the accelerators, the enterprise-
class platform includes stream computing, Hadoop-based
analytics, visualization and discovery, data warehousing and
information integration and governance capabilities.
To learn more about the platform, please visit: ibm.com/
software/data/bigdata/enterprise.html
For more information
To learn more, please contact your IBM representative or IBM
Business Partner about how the IBM accelerators included with
InfoSphere Streams and InfoSphere BigInsights can help you
solve your big data business challenges.
• ibm.com/software/data/infosphere/streams
• ibm.com/software/data/infosphere/biginsights
© Copyright IBM Corporation 2013
IBM Corporation
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Produced in the United States
January 2013
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This document is current as of the initial date of publication and may
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The performance data and client examples cited are presented for illustrative
purposes only. Actual performance results may vary depending on specifc
confgurations and operating conditions. THE INFORMATION IN
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