Text analytics and natural language processing are not new concepts. Most knowledge management professionals have been grappling with these technologies for years. From the KM perspective, these technologies share the same fundamental purpose: They help get the right information to employees at the right time.

Tools help extract data insights to grow revenue, audience, engagement

Data analytics in the cloud has become an integral part of enterprise businesses. Big data analytics systems, however, still lack the ability to take user performance goals and budgetary constraints for a task, collectively referred to as task objectives, and automatically configure an analytic job to achieve these objectives. This paper presents a data analytics optimizer that can automatically determine a cluster configuration with a suitable number of cores as well as other system parameters that best meet the task objectives. At a core of our work is a principled multi-objective optimization (MOO) approach that computes a Pareto optimal set of job configurations to reveal tradeoffs between different user objectives, recommends a new job configuration that best explores such tradeoffs, and employs novel optimizations to enable such recommendations within a few seconds. We present efficient incremental algorithms based on the notion of a Progressive Frontier for realizing our MOO approach and implement them into a Spark-based prototype. Detailed experiments using benchmark workloads show that our MOO techniques provide a 2-50x speedup over existing MOO methods, while offering good coverage of the Pareto frontier. When compared to Ottertune, a state-of-the-art performance tuning system, our approach recommends configurations that yield 26\%-49\% reduction of running time of the TPCx-BB benchmark while adapting to different application preferences on multiple objectives.

Visualization-oriented natural language interfaces (V-NLIs) have been explored and developed in recent years. One challenge faced by V-NLIs is in the formation of effective design decisions that usually requires a deep understanding of user queries. Learning-based approaches have shown potential in V-NLIs and reached state-of-the-art performance in various NLP tasks. However, because of the lack of sufficient training samples that cater to visual data analytics, cutting-edge techniques have rarely been employed to facilitate the development of V-NLIs. We present a new dataset, called Quda, to help V-NLIs understand free-form natural language. Our dataset contains 14;035 diverse user queries annotated with 10 low-level analytic tasks that assist in the deployment of state-of-the-art techniques for parsing complex human language. We achieve this goal by first gathering seed queries with data analysts who are target users of V-NLIs. Then we employ extensive crowd force for paraphrase generation and validation. We demonstrate the usefulness of Quda in building V-NLIs by creating a prototype that makes effective design decisions for free-form user queries. We also show that Quda can be beneficial for a wide range of applications in the visualization community by analyzing the design tasks described in academic publications.

Selecting an appropriate model to forecast product demand is critical to the manufacturing industry. However, due to the data complexity, market uncertainty and users' demanding requirements for the model, it is challenging for demand analysts to select a proper model. Although existing model selection methods can reduce the manual burden to some extent, they often fail to present model performance details on individual products and reveal the potential risk of the selected model. This paper presents DFSeer, an interactive visualization system to conduct reliable model selection for demand forecasting based on the products with similar historical demand. It supports model comparison and selection with different levels of details. Besides, it shows the difference in model performance on similar products to reveal the risk of model selection and increase users' confidence in choosing a forecasting model. Two case studies and interviews with domain experts demonstrate the effectiveness and usability of DFSeer.

What Is Real-Time Conversation Analytics?

In contrast to post-call conversation analytics, which provides insights after the fact, real-time call conversation analytics can point them out at present times.

In this blog, I will walk through the essential steps to build a web app that can analyze call conversations in real-time to assist an agent. Once we’re finished, we’ll have an app which will:

An automated method for cueing a high resolution video camera to a mobile object involves first detecting the presence of an object by a wide-area surveillance asset such as a radar and using the radar's positional information to cue the video camera iteratively, while updating the positional information each time. Then, a video analytics algorithm detects the object and generates more accurate positional and rate information on the object, which is then used to cue the video camera into a higher resolution setting for classifying/identifying the object. Once the object is identified, the positional and rate information is updated and the updated information is used to further cue the video camera into a higher resolution setting for recording a video clip of the moving object while the video camera is dynamically steered.

Techniques are described for automatic interval metadata determination for intermittent time series data. In one example, a method for determining intermittent time series interval metadata includes detecting one or more time variables in a time series data set. The method further includes determining whether the one or more time variables are intermittently regular. The method further includes determining one or more respective time intervals for the one or more time variables. The method further includes determining the parameters of intermittency for the one or more time variables. The method further includes generating an output comprising information about the one or more time variables based on the one or more respective time intervals and the parameters of intermittency for the time variable.

The company has designed a new system in collaboration with Microsoft to understand customer shopping behaviour, likening it to using Google-like analytics offline.

By Marc Biel

Ninety percent of Facebook’s active daily users access Facebook through mobile, making it an ideal platform to capture new app users.

IBM Cognos Analytics Server 11.1.5 Microsoft Windows Multilingual

IBM Planning Analytics for Microsoft Excel 2.0.48 32-bit Microsoft Windows Multilingual

IBM Cognos Analytics for Jupyter Notebook 11.1.6 Microsoft Windows Multilingual

IBM Planning Analytics 2.0.9 Microsoft Windows Multilingual

IBM Planning Analytics for Microsoft Excel 2.0.48 64-bit Microsoft Windows Multilingual

IBM Cognos Analytics Client 11.1.5 Multiplatform Multilingual

IBM Cognos Analytics Installer 2.0.191205 Microsoft Windows Multilingual

IBM Cognos Analytics Client 11.1.6 Multiplatform Multilingual

IBM Cognos Analytics Server 11.1.6 Microsoft Windows Multilingual

IBM Cognos Analytics Installer 2.0.2003191 Microsoft Windows Multilingual

Obtain, run, and extend a Node.js starter application that uses the IBM Cloud Geospatial Analytics service. With the Geospatial Analytics service, you can monitor moving devices from the Internet of Things. The service analyzes a device message stream from MQTT and tracks device locations in real time with respect to one or more geographic regions.

If you are already using IBM Db2 Analytics Accelerator and Tivoli Decision Support for z/OS (TDSz), you can now directly load data to Db2 Analytics Accelerator with the TDSz schema, without storing data in Db2 tables. With this feature, you can save CPU and space on expensive storage and also store and query data over a longer period at a more detailed level. This tutorial shows you how.

Deploy and extend an Internet of Things (IoT) Connected Vehicle starter kit on IBM Cloud with the Internet of Things Platform and Geospatial services. The starter kit enables you to simulate, view, and manage vehicles driving through a city and set up geofences for notification.

Our world is becoming increasingly intelligent, interconnected, and instrumented, resulting in massive amounts of data being collected. This data is a treasure trove of information that can be mined to improve service, increase sales, determine risk, or make operations more efficient.

Analysis of such large amounts of data, often called analytics, is increasingly desired by governments and businesses alike.

IBM announced today that it has been recognized as the winner in the Business Analytics category and the Enterprise Network Storage sub-category in CRN’s 2017 Annual Report Card (ARC) awards program. The Annual Report Card summarizes results from a comprehensive survey that details solution provider satisfaction across product innovation, support and partnership for the hardware, services and software vendors they team up with. The vendors with the highest ratings are named to the prestigious Annual Report Card list of winners and celebrated as best-in-class by their partners. The results also provide the IT vendor community with valuable feedback—directly from their solution providers—that can be used to refine product offerings, enhance support offerings and improve communication with partners.

IBM announced today that Southern Connecticut State University (SCSU) has integrated IBM Watson Analytics to help create new services and programs based on data-driven insights to improve students’ academic achievement and development.

IBM today announced a new agreement with Walgreens, one of the country’s largest drugstore chains, to deploy IBM retail analytics at more than 8,100 locations nationwide to help improve the efficiency of field service support for these locations.

IBM today announced the Integrated Analytics System, a new unified data system designed to give users fast, easy access to advanced data science capabilities and the ability to work with their data across private, public or hybrid cloud environments.

IBM today announced a research collaboratory in Singapore, where researchers from IBM intend to collaborate with scientists and engineers from public agencies in Singapore to improve the quality of its urban services. The focus of this research effort will be to use sensor networks to more effectively model, predict and manage the use of natural and physical infrastructure resources – water, transport and energy.  

IBM (NYSE: IBM) reinventa hoy el correo electrónico empresarial con una nueva oferta de colaboración social freemium que utiliza la tecnología de analítica integrada para dar a los empleados una nueva forma de conversar, encontrar a las personas y la información correcta rápidamente, y hacer su trabajo de manera eficiente. IBM Verse es el resultado de una inversión de US$100 millones en diseño e innovación, y reúne las plataformas líderes cloud, analytics, social y de seguridad de la compañía para transformar el futuro del trabajo.

IBM (NYSE:IBM) today announced that healthAlliance, New Zealand’s largest shared services organisation for the health sector, will use an IBM Netezza analytics appliance to provide faster, more accurate clinical and business analysis to better manage hospital resources and services.

IBM today announced new systems highly-tuned and optimised to help clients more quickly draw insights from vast amounts of data to anticipate emerging business trends, capture new opportunities and avoid risks. These new optimised systems support all environments in a data center, enabling clients to handle higher volumes of transactions and analyse data where it resides.

IBM today announced a new analytics-based tool for its data centre consolidation and relocation services which help clients gain up to 50% operational savings from the consolidation of IT assets and data centres.

IBM (NYSE: IBM) today announced a business consulting and technology partnership with Yarra Valley Water to enhance the management and functionality of its assets and customer service to over 1.7 million people and 50,000 businesses throughout Melbourne now and in the future. Through this partnership Yarra Valley Water will gain access to critical insights to maximise asset performance and improve customer service outcomes across its operations.

SAS Visual Analytics can be configured to send a notification to specific users when report objects contain data that meets certain criteria. This SAS note contains frequently asked questions about setting up alerts.

If your SAS Visual Analytics report contains many sections and objects, you might encounter performance problems when you are editing the report.   A hot fix is planned for this issue.

A custom sort might be sorted incorrectly when the data item is used in a custom category, which is then used in a hierarchy. The issue can occur in the following scenario:

When you attempt to save a report distribution in SAS Visual Analytics Designer, you might see the error shown in the following display:  imgalt="" src="{fusion_65868_1_distributionerror.png}" />

Common filters are filters that can be shared between objects in your reports. Common filter benefits include 1) Easy to assign the same filter conditions to other report objects, 2) When you edit a common filter, it is updated everywhere that the common filter is used, and 3) A common filter is available for the entire report, across pages.

Using common filters in SAS Visual Analytics was published on SAS Users.

With increasing interest in Continuous Integration/Continuous Delivery (CI/CD), many SAS Users want to know what can be done for Visual Analytics reports. In this article, I will explain how to use Python and SAS Viya REST APIs to extract a report from a SAS Viya environment and import it into another environment.

Continuous Integration/Continuous Delivery – Using Python and REST APIs for SAS Visual Analytics reports was published on SAS Users.

If you’re in state government, you're well aware of the unprecedented challenges that come with off-the-charts unemployment numbers due to COVID-19. Crashing websites at state unemployment insurance offices and multi-hour phone hold times for the millions of people who've lost their income due to the pandemic are only the most [...]

How analytics is helping state governments head off unemployment claims fraud was published on SAS Voices by Carl Hammersburg

Common filters are filters that can be shared between objects in your reports. Common filter benefits include 1) Easy to assign the same filter conditions to other report objects, 2) When you edit a common filter, it is updated everywhere that the common filter is used, and 3) A common filter is available for the entire report, across pages.

Using common filters in SAS Visual Analytics was published on SAS Users.

With increasing interest in Continuous Integration/Continuous Delivery (CI/CD), many SAS Users want to know what can be done for Visual Analytics reports. In this article, I will explain how to use Python and SAS Viya REST APIs to extract a report from a SAS Viya environment and import it into another environment.

Continuous Integration/Continuous Delivery – Using Python and REST APIs for SAS Visual Analytics reports was published on SAS Users.

For beginners, how marketers can learn to create advanced data models from time series data

Using R programming, marketers can create a marketing mix model to determine how sustainable their audience channels are, and make better ad spend decisions. Here's how

Here's a new angle on GDPR, likely one of many angles people haven't yet considered: Does web session recording count as collecting PII?

Advanced analytics programs can incorporate geospatial data. Learn how such data can be used to augment local marketing plans

Digital transformation. Yup, I said it. It's over-hyped. But as SAS Chief Operating Officer and Chief Technology Officer Oliver Schabenberger says, "It's also real and powerful. Our world is being liquefied from physical assets into virtual assets, and analog processes into digital processes - the world is turning into bits [...]

SAS Customer Intelligence 360: Analytics as a guiding light was published on Customer Intelligence Blog.

Creative government workforce approaches come in many forms. Some time ago, the US Environmental Protection Agency (EPA) offered employees an interesting choice to incentivize them to stay. Employees could either telework from home one day per week or receive $1,000. As Jon Lemon, principal industry consultant at SAS told the [...]

The post How analytics can shape the government workforce appeared first on Government Data Connection.