In this study, four efficient tools for analyzing patent documents were tested: Thomson Reuterís Aureka and Thomson Data Analyzer, Biowisdomís OmniViz, and STNís STN AnaVist. All four tools analyze structured and unstructured data alike. They all visualize the results achieved from clustering the text fields of patent documents and either provide basic statistics graphs themselves or contain filters for performing them with other solutions. The tools were tested with two cases, evaluating their ability to offer technology and business intelligence from patent documents for companiesí daily business. Being aware of the state of the art of relevant technology areas is crucial for a companyís innovation process.
Mesos, the kernel of the Mesosphere DCOS, is a 6-year-old Apache open-source project, conceived at the University of California, Berkeley, that was announced as a joint collaboration with Mesosphere at DockerCon EU in December 2014. The company has come a long way in the nine months since then, as more and more enterprises retool their data centers to run DCOS. Mesosphere DCOS is a highly scalable engine that enables the running of services and applications across a cluster of machines in a data center or cloud. It is highly container-driven. It combines the Apache Mesos cluster manager with a number of open-source and proprietary components and allows services to be deployed and managed through both a custom Web UI and command-line interface.
Based on the Gen5 platform architecture that Brocade uses for its storage and networking products, the Brocade Analytics Monitoring Platform comes in a 2U form factor that can be configured with up to 24 Fibre Channel ports. The appliance itself sports two dedicated multi-core processors for frame processing and an onboard solid-state disk drive. From a software perspective, it runs an implementation of Brocade’s Fabric OS (FOS) that includes analytics capabilities and can be integrated with Brocade Network Advisor software. Rondoni said IT organizations can use the platform to generate customized reports to correlate and summarize trends and specific events.
In-room tablets at hotels serve as media hubs, control centers, and information desks. Guest enters the room - curtains open, music plays and climate control switches on with option for guests to personalize these settings. Guests leave the room and the settings are reset to defaults, but guests' personalized settings can be saved and automatically applied upon future visits. Bathroom mirror as interactive display for news, weather and messages with ability to pair with smartphones or tablets. Eletro responsive fibers in pillows monitors blood pressure, sleep patterns and stress levels.
The value and power of a data lake are often not fully realized until we get into our second or third analytics use case. Why is that? Because it is at that point where the organization needs the ability to self-provision an analytics environment (compute nodes, data, analytic tools, permissions, data masking) and share data across traditional line-of-business silos (one singular location for all the organization’s data) in order to support the rapid exploration and discovery processes that the data science team uses to uncover variables and metrics that are better predictors of business performance. The data lake enables the data science team to build the predictive and prescriptive analytics necessary to support the organization’s different business use cases and key business initiatives.
Windows 10 is Microsoft's effort to recapture many enterprise users who balked at Windows 8's mobile-focused interface and to finally move the last Windows XP and Vista holdouts onto a newer OS. Beyond the new features, security updates, and its platform-unifying design, Windows 10 marks a significant shift in how Microsoft's characterizes its flagship operating system. Microsoft is encouraging people to think of Windows 10 as a "service". Instead of releasing a new numbered version of Windows every few years, the company will continuously release new features and updates. Microsoft has committed to support Windows 10 for a decadeafter the July, 2015 launch.
Companies that know and understand the similarities and differences across their information, data and storage media, along with their associated lifecycle management and tiered protection, can unlock value while removing complexity and costs to sustain growth. Organizations should start by revisiting information,data and storage media management, along with their corresponding lifecycles. Then, they should focus on what can be accomplished today in comparison to how processes worked in the past. This will allow companies to distinguish between their needs and wants. Businesses can then begin to remove costs by finding and addressing data protection complexities at the source, as opposed to cutting service.
Analytical models have greatly evolved both in the depths of the mathematical techniques as well as the wide-spread application of the results. The methodology to create analytical models, however, is not well described, as can be seen by the fact that the job of the analytic practitioners (currently called data scientists; older names are statistician, data analyst and data miner) involve a lot of tacit knowledge, practical knowledge not easily reducible to articulated rules2. This informality can be seen in many areas of analytical modeling, ranging from the project methodology, the creation of the modeling& validation data, the analytical model-building approaches to model reporting. The focus of this document is project methodology.
Services were modeled based on business capability model and the first release went well. They were XML over JMS sync services and primarily focused on delivering the capabilities required for claims platform exposed to Agents, web and voice channel application. It gave us the ability to deploy frequent, small changes and A/B feature support seamlessly for our applications.When the requirements were incrementally added (and they always were) it was very hard to release the solution rapidly because of the integration complexity between applications and the consumers. Integration, functional testing, and production release required tight coordination.
It’s important to quickly understand that cloud computing isn’t going anywhere. In fact, the proliferation of cloud computing and various cloud services is only continuing to grow. Recently, Gartner estimated that global spending on IaaS is expected to reach almost US$16.5 billion in 2015, an increase of 32.8 percent from 2014, with a compound annual growth rate (CAGR) from 2014 to 2019 forecast at 29.1 percent. There is a very real digital shift happening for organizations and users utilizing cloud services. The digitization of the modern business has created a new type of reliance around cloud computing. However, it’s important to understand that the cloud isn’t just one platform. Rather, it’s an integrated system of various hardware, software and logical links working together to bring data to the end-user.
Quote for the day: "The final test of a leader is that he leaves behind him in other men, the conviction and the will to carry on." -- Walter Lippmann