anomaly detection

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Published By: Adobe     Published Date: Aug 02, 2017
With the advanced analytics capabilities in Adobe Analytics and the testing and targeting capacity of Adobe Target, itís easier than ever to realise the potential of data-driven marketing. From creating a complete view of each customer across touchpoints and along their journey, to using predictive analytics, advanced anomaly detection and machine learning to understand behaviours and needs, you can use data to plan, create and optimise the experiences that matter to you and your customers.
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data management, data system, business development, software integration, resource planning, enterprise management, data collection
    
Adobe
Published By: Cisco EMEA Tier 3 ABM     Published Date: Nov 13, 2017
Encryption technology has enabled much greater privacy and security for enterprises that use the Internet to communicate and transact business online. Mobile, cloud and web applications rely on well-implemented encryption mechanisms, using keys and certificates to ensure security and trust. However, businesses are not the only ones to benefit from encryption.
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anomaly detection, trust modeling, event classification, entity modeling, relationship building, internet scrapers
    
Cisco EMEA Tier 3 ABM
Published By: HP Enterprise Business     Published Date: Jan 20, 2017
A fresh approach to cloud-based website load testing is proving more effective in identifying and isolating application performance anomalies.
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HP Enterprise Business
Published By: IBM     Published Date: Jun 16, 2009
Establish and Maintain Secure Cardholder Data with IBM Payment Card Industry Solutions.
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pci dss, ibm, mmdg, payment card industry, assessment, design, deployment, management, report on compliance, roc, vulnerabilities, threat, solutions, compliance, secure, cardholder, encryption, hospitality, retail, transportation
    
IBM
Published By: SAS     Published Date: Oct 18, 2017
With enhanced regulatory pressure, banks must continuously evaluate their risks. To meet these demands, the AML industry has turned to analytical/statistical methodologies to reduce false-positive alerts, increase monitoring coverage and reduce the rapidly escalating financial cost of maintaining their AML programs. An effective AML transaction monitoring strategy includes segmenting the customer base by analyzing customer activity and risk characteristics in order to monitor them more effectively. This paper explains how to blend both quantitative and qualitative methods to tune scenarios to identify the activity that poses the most risk to the bank.
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SAS
Published By: Schneider Electric     Published Date: Aug 15, 2017
Schneider Electric is integrating datacenter infrastructure management (DCIM) software, big-data analytics and cloud services into the management of customersí datacenters. Its recently launched StruxureOn cloud offering signals a new wave in datacenter operations, using a combination of machine learning, anomaly detection and event-stream playback to give operators real-time insights and alarming via their smartphones. More capabilities and features are planned, including predictive analysis and, eventually, automated action. Schneiderís long-term strategy is to build a partner ecosystem around StruxureOn, and provide digital services that span its traditional datacenter business.
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incident tracking, historical trending, troubleshooting, operational analysis, prediction model, schneider equipment, maintenance, firmware updates
    
Schneider Electric
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