C2090-013 exam Dumps Source : IBM SPSS Data Mining for industry Partners v2
Test Code : C2090-013
Test denomination : IBM SPSS Data Mining for industry Partners v2
Vendor denomination : IBM
: 25 real Questions
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a accepted analogy announces that facts is "the brand unique oil," so regard of facts mining as drilling for and refining oil: facts mining is the skill through which companies extract value from their facts.
in more functional phrases, records mining comprises examining records to study for patterns, correlations, traits and anomalies that can live huge for a particular enterprise. As such, it's intently regarding stout records, a bigger time age that encompasses the various makes exercise of of facts to engage into account developments.
as an example, facts mining can assist agencies identify their premier purchasers. organizations can exercise facts mining recommendations to research a particular consumer's aged buy and predict what a client may live likely to purchase sooner or later. it could besides spotlight purchases which are out of the regular for a customer and may point out fraud.
businesses can exercise to locate inefficiencies in manufacturing approaches, competencies defects in products or weaknesses in the provide chain. a mighty master information management approach contains information mining.https://o1.qnsr.com/log/p.gif?;n=203;c=204651744;s=9478;x=7936;f=201802271700250;u=j;z=TIMESTAMP;a=20403940;e=i
commonly, data mining recommendations are used to analyze structured statistics that resides in information warehouses. youngsters, agencies besides exercise statistics mining to aid extract insights from their outlets of unstructured facts that may dwell in Hadoop or a further category of information repository.
today, statistics mining on bar nobody types of information has become piece of a certainly not-ending quest to capitalize aggressive expertise.
leap to:historical past of statistics Mining
some of the first articles to exercise the phrase "statistics mining" changed into published with the aid of Michael C. Lovell in 1983. at the time, Lovell and a lot of other economists took a fairly detestable view of the follow, believing that data may lead to incorrect conclusions when now not advised by using talents of the discipline remember.
however by way of the Nineteen Nineties, the theory of extracting cost from facts by way of picking out patterns had turn into plenty more frequent. Database and facts warehouse providers bar nobody started using the buzzword to market their software. And groups bar nobody started to develop into watchful about the edge benefits of the follow.
In 1996, a group of corporations that included Teradata and NCR led a challenge to standardize and formalize information mining methodologies. Their work resulted within the go-business timehonored technique for statistics Mining (CRISP-DM). This open common breaks the information mining technique down into six phases:
agencies like IBM continue to promote the CRISP-DM model to today, and in 2015, IBM released an up to date version that improved the primary mannequin.
within the early 2000s, internet agencies begun to peer the vigour of information mining, and the apply really took off. while the phrase "information mining" has on the grounds that been eclipsed by way of different buzzwords like "facts analytics," "huge records" and "machine gaining information of," the manner remains an essential component of company practices. basically, it's objective to pretension that data mining has turn into a de facto a piece of operating a latest enterprise.types of records Mining
records scientists and analysts exercise a variety of facts mining strategies to achieve their desires. one of the crucial most typical include perquisite here:
statistics Mining method
instance exercise Case
finding corporations and subgroups within statistics
Sorting records into classes
making a altenative on related items of statistics
discovering records that does not apt the general patterns
Predicting the surely consequence from given variables
Predictive analytics and forecasting
analyzing written words
Condensing information so that it is less demanding to engage note
Graphingconcepts related to statistics Mining
records mining overlaps with several linked terms, and americans on occasion exercise these terms in reference to equivalent ideas. probably the most most common linked concepts include the following:facts mining vs. KDD
within the late Eighties and early Nineteen Nineties, academics frequently mentioned potential discovery in databases (KDD). The formal definition of the KDD manner protected five levels:
beneath this framework, information mining is the equivalent of records analysis and is a subcomponent of KDD. In practice, however, people regularly used statistics mining and KDD interchangeably. Over time, statistics mining grew to become the favourite time age for each procedures, and nowadays, most individuals exercise "statistics mining" and "knowledge discovery" to spell the equal thing.statistics mining vs. machine discovering
desktop studying is the arm of abysmal discovering and artificial intelligence that seeks to provide computers the faculty to study with out being programmed. a few of the ideas used in statistics mining — primarily clustering, classification and regression — are additionally utilized in machine studying. for that reason, some americans harmonize with computing device learning to live a subset of statistics mining.
however, other individuals bicker that there are subtle changes between the two. they are epigram that records mining finds the patterns within the records, after which machine getting to know makes exercise of the effects of information mining to live taught whatever unique in regards to the statistics.
Whichever point of view you select, the two ideas naturally overlap one yet another.statistics mining vs. huge data analytics
people commonly exercise the terms "data mining" and "huge records analytics" or "facts analytics" to suggest the equal factor. Some americans quibble that records mining will besides live accomplished on small data sets in addition to "large records." And others mumble that information analytics can include thoughts other than information mining, so records mining is a subset of analytics.
In follow, these terms are nearly interchangeable. or not it's simply that "data mining" was a accepted buzzword in the Nineties and early 2000s, whereas "analytics" has develop into the more usual buzzword these days.records Mining Examples
well-nigh each enterprise on this planet makes exercise of information mining, so the examples are just about limitless. One very general way that retailers exercise information mining is to analyze customer purchases after which ship shoppers coupons for items that they may wish to purchase sooner or later.
company's more and more refined exercise of records mining has made many buyers uncomfortable. within the U.S., Congress and the Federal alternate fee (FTC) occupy convened hearings on records privacy, besides the fact that children these efforts haven't yet resulted in complete law.
Europe has been ought to faster to act on data privateness considerations. final might also, the everyday facts coverage legislation (GDPR) went into effect, and it affects each solid with any information regarding european citizens.
among different things, the legislations requires businesses to garner consent to manner facts, to delete a subject's statistics in the event that they request it, to position ample protection measures in spot to give protection to records, and to notify individuals straight away if their data has been worried in a data breach.
Failing to comply might influence in fines of up to four% of an organization's total international earnings. industry watchers predict that GDPR and other legislations may occupy an incredible occupy an consequence on on records mining, and the european has already fined Google €50 million for inadequate compliance with the legislations.statistics Mining equipment
businesses occupy a wide selection of proprietary and open supply statistics mining gear available to them. These tools encompass facts warehouses, ELT tools, information cleansing equipment, dashboards, analytics equipment, textual content evaluation equipment, company intelligence gear and others.
(Tribune word provider) -- IBM on Tuesday brought its first cloud-primarily based facts-mining and -evaluation services that occupy arisen out of its unique partnership with Twitter.
the U.S. know-how mammoth is providing to trawl via Tweets posted on the micro-running a blog web site, evaluate and analyze them for the edge of its industry clients.
A half one billion Tweets seem every day on Twitter and, with the assist of shrewd analytical equipment, tendencies can live identified and customer requests can besides live fished out of that heap of information, Alistair Rennie, manager of IBM's analytics unit, said at CeBIT, the realm's largest suggestions expertise fair, which is taking region this week within the northern German city of Hanover.
IBM plans to investigate Tweets in precise time via cloud know-how and its Watson supercomputer, which has been trained to live watchful Tweets. purchasers in such areas as telecommunications, retail sales and fitness custody may then fetch your hands on information that interests them.
such a aspect has not been feasible earlier than and may essentially exchange the rules of the video game for businesses, Rennie talked about.
The initiative was announced after IBM and Twitter started their partnership at the conclusion of October.
IBM Analytics' Bob Picciano stated cloud computing has elementally altered the dynamic of the information technology transformation.
The company become an early investor in the so-referred to as massive information enviornment of the business, the evaluation of giant masses of records.
"now they occupy invested 24 billion greenbacks in the region in fresh years," Picciano observed.
©2015 Deutsche Presse-Agentur GmbH (Hamburg, Germany) dispensed by using Tribune content agency, LLC
PMML recasts the statistics warehouse as a turnkey platform for real-time statistics mining.
It’s been eight years in the making, however the predictive modeling mark-up language (PMML) is on the verge of going mainstream.
PMML is an XML mark-up language that’s used to picture statistical and facts-mining fashions. Its principal selling factor is that it gives PMML-compliant applications a simple technique to share information fashions with different PMML-mindful equipment. It’s stewarded by using the statistics Mining neighborhood, an industry consortium that comprises a Who’s Who of data mining and relational database providers, including lots of widely wide-spread faces—reminiscent of IBM Corp., Microsoft Corp., Oracle Corp., SAP AG, SAS Institute Inc., SPSS Inc., and NCR Corp. subsidiary Teradata.
One advantage, proponents say, is that PMML easily brings information mining down from the mountain—i.e., the rarefied realm of the SAS or SPSS guru—and democratizes it. “facts mining can live checked out as two fundamental methods. The building of the model nevertheless must live achieved by means of the SAS or SPSS experts, but PMML helps with the deployment, taking that statistics model and executing it,” says Arlene Zaima, advanced analytic program manager with Teradata.
in consequence, Zaima says, users who aren’t chummy with the intricacies of SAS or SPSS can work with no custody with pre-built PMML records models. “The deployment [by users] is done an detestable lot greater generally—day by day and even as much as the minute—and that’s what PMML addresses: the execution for the mannequin.”
Like yet another lengthy-gestating general, the XML query language (XQuery), PMML has been within the works for some time. however unlike XQuery, PMML has continuously advanced over time, starting with the PMML 1.1 unlock five years in the past. these days, PMML is in version three.0 and groups such as Teradata, IBM, Oracle, and Microsoft tender various levels of sheperd for the technology.
PMML in follow
Dan Friedman, a essential with utility marketing consultancy DHF Consulting, says he’s labored with several application companies which occupy included support for PMML into their items. The explanations, he says, are many, but a common driver is a necessity to handle the very different necessities of facts-mannequin building and records-model execution—the same division highlighted through Teradata’s Zaima.
“which you could believe of predictive information as coming in two items—runtime and design time,” he says. “Design time is performed offline, where americans are likely to exercise subject statistical programs like SPSS, SAS, and so forth. this may engage weeks or months to achieve and is completed by totally really wonderful analysts.”
The expertise of PMML, says Friedman, is that it will possibly support accelerate statistics mannequin runtime execution. “Runtime is the way you integrate this model into an operational device, like a CRM or monetary system. typically, you set up the model to the run-time and exercise it to give a ranking that is then acted upon with industry rules or another company logic. This scoring is achieved in real time and takes [less than] one 2nd,” he explains.
Friedman, youngsters, facets to still an additional division—between stats geeks and company-domain consultants. PMML can assist bridge this chasm, too. “The issue is, the talents essential to Do the runtime piece are a wonderful deal different than the capabilities to Do the modeling piece. In practice, the runtime is built via domain specialists that truly live watchful the company method. They don't seem to live leading edge ‘laptop discovering specialists’ or stats jockeys. The stats guys live watchful of the mathematics, but not the industry manner,” he says. “So, the runtime people would like to (1) exercise other businesses’ modeling tools, [and] (2) do sure they … can leverage the top-quality that the modeling gear can provide. because the run-time guys aren't specialists, they seem to build in force requirements to do sure they're lined for many of the fashions that americans necessity to build these days and tomorrow.”
in this admire, says Toby Dunn, a SAS skilled with the department of training in a in exact Southwestern state, PMML may live the most pragmatic option for fixing many vexing company problems. Dunn may still know: in a former career, he worked for a corporation that developed a series of statistics fashions for banks and credit card groups. “These fashions covered credit score scoring, salary prediction, and fetch in touch with-core queuing. The models occupy been developed the usage of SAS and deployed in a proprietary Java program at the customer’s web site,” he explains.
One problem with this approach became that the company’s Java application mandatory to live in a position to race the statistics models developed via Dunn and his colleagues as well as the present and future records models developed with the aid of consumers.
“PMML solved this difficulty,” he says. “PMML changed into used for 2 explanations. First, it became a commonly used and sturdy ordinary tag set, which anyone could proceed and study up on the internet. So regardless of who turned into constructing the model to live deployed, bar nobody they necessary to Do became give that mannequin within the circumstantial version of PMML to the customer and that they could at once and simply implement it into the device. Secondly, it will Do the calculations required in order for the proprietary Java software to Do its job and document lower back to the consumer.”
PMML and the Primacy of the records Warehouse
Teradata’s Zaima identifies nevertheless a different division (and simmering supply of misunderstanding) in the information-mining area.
“There’s always … a division between the analytic modelers and the SQL jockeys” who manage the facts warehouse, she observes, because of the fairly inelegant way (from the perspective of analytic modelers, anyway) through which statistics mining is historically achieved.
“The data warehouse has at bar nobody times been a crucial piece of analysis, because that’s where the information is sort of reconciled and pulled together,” she notes. in lots of instances, besides the fact that children, information mining isn’t achieved within the records warehouse; as a substitute, information is extracted from the warehouse and loaded into an external repository.
This approach has obvious prices, notwithstanding, when it comes to both performance and timeliness. “These problems can besides live solved with SQL models, actually, Teradata’s first system was to boost SQL models for Teradata Warehouse Miner, however as you comprehend, loads of organizations occupy subject standards around SAS or SPSS,” Zaima says. “They’ve invested so plenty in this competencies, and these analysts aren’t affordable. They necessity to live capable of leverage their investment in application and materials, but they wish to leverage the vigour of in-database [mining].”
PMML permits them to Do that. “It just eliminates that want of pulling the data out into yet another server, so it reduces the time to market and speeds time to deployment. Now [the analytic modelers] can just say, ‘Take my PMML model and just race it in the database.’ There doesn’t ought to live any recoding [to SQL].”
standard for Sharing information Mining models Falls Shorthttp://www.esj.com/business_intelligence/article.aspx?EditorialsID=7059
Stephen Swoyer is a Nashville, TN-primarily based freelance journalist who writes about expertise.
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The global machine learning market totaled $1.4 billion in 2017, according to BCC Research. It is estimated to top $8.8 billion by 2022, a jaw-dropping compound annual growth rate (CAGR) of 43.6 percent.
When considering which machine learning vendor to select, weigh these factors:
The machine learning landscape is changing rapidly. Machine learning start-ups are constantly jumping into the space. Established vendors are introducing a variety of offerings that exercise machine learning in one shape or another. Sorting through the choices and options can prove confusing. We’ve identified eight top vendors in the machine learning space, based on the features they offer, analyst reviews, customer testimonials and independent research.
Alteryx offers a unified platform that addresses data, analytics and machine learning. The vendor strives to minimize the necessity for data scientists by creating unique and improved partnerships among IT, analytics experts and lines of industry through a collaborative approach.
The product is available in both a code-free and code-friendly format. It uses a drag-and-drop workflow interface and ties into third party demographic, firmographic and spatial data. The company is rated as a “Leader” on the Gartner Magic Quadrant (MQ). Alteryx is affecting toward powerful automation and rule-based recommendations, Gartner noted.
Alteryx offers integration with numerous major partners, including Tableau, AWS, Teradata, Microsoft, DataRobot, Salesforce, Oracle, Cloudera and Qlik. Machine learning functions feature parallel model analysis with predictive analytics, along with the faculty to automate workflows and various processes.
Read their in-depth review of AlteryxSee user reviews of AlteryxAWS SageMaker
The fully managed machine learning service allows data scientists and developers to build and train machine learning models and insert them into applications. This makes it possible to race parallel models, conduct elastic distributed training, and tackle A-B testing along with other tasks.
SageMaker is a fully managed AWS service that includes an integrated Jupyter authoring notebook instance. This aids in exploration and analysis by offering abysmal visibility into data sources. SageMaker offers a set of algorithms optimized for the environment and it supports proprietary algorithms and custom training scripts.
Sagemaker besides features integration with Docker containers and Apache Spark. Together with AWS, it’s possible to exercise the integrated SageMaker console to launch and scale projects quickly. The platform supports HIPAA and other compliance standards.
Read their in-depth review of SageMakerSee user reviews of SageMakerGoogle Machine Learning Engine
Google Cloud Machine Learning (ML) Engine is a fully managed platform designed for developers and data scientists. The product allows users to construct lofty quality models using multiple machine learning frameworks, including scikit-learn, XGBoost, Keras, and TensorFlow.
The latter is a sophisticated abysmal learning framework that powers many Google products, from Google Photos to Google Cloud Speech. It allows users to deploy machine learning into production without the necessity for Docker containers. The highly scalable environment—which accommodates training on managed clusters, delivers a framework for automatically designing and evaluating model architectures. It is designed to accommodate existing workflows, and it scales to handle batch prediction and online prediction requirements.
Other key features include: automatic resource provisioning, the exercise of portable models, server-side processing and integrated cloud data storage.
Read their in-depth review of Google Machine Learning EngineSee user reviews of Google Machine Learning EngineIBM Watson Studio
IBM was a pioneer in the artificial intelligence (AI) and machine learning spaces. Today, Forrester ranks the company as a “Leader” among multimodal predictive analytics and machine learning solutions providers.
IBM Watson Studio is designed to accommodate a variety of independent platforms and various types of power users. This includes data engineers, app developers and data scientists. The result is strong collaboration capabilities. Among its top features: a robust architecture, strong algorithms and a powerful faculty to execute machine learning.
The Watson Studio is piece of an extensive collection of technologies and solutions that are available under the IBM Watson umbrella. It is designed to accommodate IBM’s SPSS modeling workflows but besides plug in open source machine learning libraries and notebook-based interfaces. The solution is available in the cloud on or on the desktop. It incorporates visual drag-and-drop tools.
Read their in-depth review of IBM Watson StudioSee user reviews of IBM Watson StudioMicrosoft Azure Machine Learning Studio
Azure Machine Learning Studio has emerged as a leading solution in the managed cloud space. It delivers a visual tool that aids developers, data scientists and non-data scientists in designing machine learning pipelines and solutions that address a wide variety of tasks. The platform offers a browser-based, visual drag-and-drop authoring environment that requires no coding. Gartner ranks Microsoft a “Visionary” in its MQ. The solution offers a lofty plane of flexibly, extensibility and openness.
Azure ML Studio besides delivers comprehensive capabilities across the complete range of descriptive, diagnostic, predictive and prescriptive analytic types. Microsoft is continuing to expand the features and capabilities within Azure Machine Learning. This includes promoting abysmal learning through the Microsoft Cognitive Toolkit (formerly CNTK) as well as the joint ONNX open standard for neural networks. Microsoft besides plans to automate a growing number of functions within the Azure Machine Learning platform.
Read their in-depth review of Microsoft Azure Machine Learning StudioSee user reviews of Microsoft Azure Machine Learning StudioRapidMiner
RapidMiner offers data prep, machine learning and model deployment tools that deliver abysmal insight into data. It delivers a platform that is designed to tackle a variety of stout data and analytics requirements.
In the machine learning space, the company has established itself as a heavyweight. It delivers a simple-to-use visual environment that incorporates automated model creation along with suggestions, guides and information about what approach might work best for a given situation. RapidMiner’s features and lofty plane of flexibility occupy established it as a accepted altenative among machine learning solutions.
Data scientists and non-data scientists alike can easily inspect and customize projects and build on results. RapidMiner is ranked as a “Leader” in both the Gartner MQ and the Forrester Wave. It is among the highest rated data and machine learning solutions at Gartner Peer Insights.
Read their in-depth review of RapidMinerSee user reviews of RapidMinerSAP Leonardo
Leonardo serves as an umbrella for a variety of SAP data products and services. It incorporates AI and machine learning through a cloud platform. This includes a machine learning foundation that allows developers, data scientists and others to create, race and deploy algorithms and machine learning apps.
The technology extracts data from text, images, natural language and other media to generate computer-based predictions. The highly scalable platform includes predefined and pretrained machine learning services, but it besides incorporates TensorFlow models. This allows users to adopt a elastic framework to training and tuning machine learning. Forrester ranks SAP as a “strong performer” in the overall predictive analytics and machine learning category and cites the breadth and depth of service as a strong point.
However, Gartner rates the company a “Niche Player” in its MQ. It noted that SAP has made considerable strides in the data and machine learning spaces but still lags behind others in terms of features and capabilities.
Read their in-depth review of SAP LeonardoSee user reviews of SAP LeonardoSAS Visual Data Mining and Machine Learning
A pioneer in the data mining and analytics spaces, SAS has established itself as a top altenative among machine learning vendors. It offers Visual Data Mining and Machine Learning (VDMML), which ranked at the standout “Leader” in the Forrester Wave and as a top “Leader” in the Gartner MQ, though the research solid notes that the overall platform has lost some ground in terms of vision and faculty to execute.
VDMML connects to numerous other products and data sources. Forrester noted that SAS has emerged as the first truly multimodal PAML solution. It uses a wizard to automatically train a model, transform it to code and plug in integrated features for automated machine learning. The unified environment besides includes robust visual tools for creating models as well as abysmal neural networks.
In addition, data scientists can embed snippets of code directly into SAS programs to further enhance capabilities, including machine learning and abysmal learning output.
Read their in-depth review of SAS Visual Data Mining and Machine LearningSee user reviews of SAS Visual Data Mining and Machine LearningData Science and Machine Learning Vendors at a Glance:
Vendor and features
Google Machine Learning Engine
IBM Watson Studio
Microsoft Azure Machine Learning
SAS Visual Data Mining and Machine Learning
Analytics and ML platform that allows users to build models in a single workflow
Hosted platform that supports supervised and unsupervised ML and abysmal learning.
Hosted platform that runs ML training jobs and predictions at scale.
Broad data science focus with cloud and desktop ML platforms.
Automated ML platform running on hosted Azure Cloud.
Highly automated ML platform suited for firms aiming to exercise machine learning broadly.
Broad and abysmal data science and ML platforms.
Multimodal PAML solution with powerful wizards to support data mining and ML. Strong automation.
Key Features and capabilities
Offers more than 250 code free pre-built tools and supports open source tools and scripting.
Zero-setup integrated Jupyter authoring notebook instance. Access to affluent array of open source frameworks and tools.
Automatic resource provisioning and monitoring for CPUs, GPUs and TPUs. Strong open source support for tools and scripting languages.
Strong visual recognition and natural classification tools. elastic approach that incorporates open source tools. Connects to IBM SPSS Modeler.
Strong descriptive, diagnostic, predictive and prescriptive analytics tools and capabilities. Strong support for open source ML tools and scripting.
Offers more than 1,500 machine learning and data prep functions, and it supports more than 40 files types. Connects to Amazon S3 and Dropbox.
Fully supports a BYOM approach. Strong open source integration. Runs in SAP’s HANA public cloud.
Includes best drill templates and in-memory processing that supports supervised and unsupervised learning.
High ratings for simplicity and usability. Complaints about the lack of robust reporting and visualization features.
High marks for the interface, configurability and usability. However, some mumble that the familiarity with AWS is crucial.
High ratings for functionality and integration.
Highly rated for features and capabilities. Some complaints revolving around using notebooks.
High ratings at Gartner Peer Review. Some complaints about support.
Among the highest rated data science and ML solutions. Users picture it as powerful and “revolutionary” though there are complaints about the lack of GPU support.
Customers give SAP lofty marks for usability and performance. Some complain about the lack of visualization tools for external data.
High ratings. Users like the powerful features and capabilities built into the platform, though some remark that there is a substantial learning curve associated with the product.
Pricing and licensing
Tiered system based on products used. Ranges from $5,195 to $33,800 per seat annually.
Complex tiered pricing model based on ML notebook instances and cloud resources consumed.
Based on resources and training hours used. Ranges from 1 cent per hour to $31 per hour for various services.
Tiered model from $99 per month per user to $6,000 per month per user or more at enterprise level.
Tiered pricing based on users and usage. Ranges from near zero to tens of thousands per user annually.
Tiered pricing ranging from $2,500 per user per year to upwards of $10,000 per user per year.
Tiered model based on node hours consumed in the cloud. Billing blocks are imbue in increments of €7.25.
December 3 2007
SPSS has announced an agreement with recently acquired industry intelligence (BI) solutions industry Objects, which will tender customers the exercise of SPSS predictive analytics data mining technology as piece of its BusinessObjects XI platform.
The partners mumble the deal will enable customers to do 'more informed decisions that can aid generate revenue, control expenses, and mitigate risk'. Patrick McCue, SPSS' VP of Global Alliances, says the partnership is 'truly a market-shaping agreement, validating the increased interest - enterprise-wide - in the value of predictive analytics technology.'
Chicago-based SPSS explains predictive analytics as 'technology which helps individuals explore past and present industry scenarios, as well as uncover trends and identify outliers, to project future outcomes'.
Business Objects, whose stock is traded on both the Nasdaq and Euronext Paris stock exchanges, has dual headquarters in San Jose, California, and Paris, France. The company was bought by industry software giant SAP less than two months ago (www.mrweb.com/drno/news7413.htm ) and has since seen its major compete Cognos snapped up by IBM (www.mrweb.com/drno/news7571.htm ).
Web sites are at www.businessobjects.com and www.spss.com.
All articles 2006-18 written and edited by Mel Crowther and/or Nick Thomas.
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