Does IBM Watson Analytics use keywords in column headers?


Is there a list of keywords that IBM Watson Analytics uses in column headers to try to determine properties? Could “Gross” be a keyword for currency? Once set in Watson Analytics, it doesn’t appear to be converted.

For example, look at the out-of-the-box “TRIRIGA Buildings & Space Allocation Data” set. Gross Area shows as currency ($). Others are too: Area/Occupant (Gross), Gross Rentable. But not all number columns are, ones that don’t include “Gross”. The UOM doesn’t appear to be defined in the properties, so it seems like it must be using the column header? I haven’t tried changing the header, but as-is, once exported, is there a way for that column to show correctly as an area instead of currency?

This question is best suited for the IBM Watson Analytics forum. There are posts on that forum that indicate that Watson Analytics does look at column names when deciding whether to add currency values, but I did not see a list of all the special keywords. Other than changing the column name before upload, I don’t know of a way to override those special keywords or modify the UOM that Watson Analytics assigns in the data set. So these questions will be better answered in the Watson Analytics forum. That forum is also the place where you can suggest enhancements around this Watson Analytics behavior.

[Admin: This post is related to the 12.15.16 post about finding information about the IBM TRIRIGA Connector for Watson Analytics, and the 11.15.16 post about the overview and demo. To see other related posts, search “Watson Analytics“.]

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Is there a way to refresh exported IBM Watson Analytics data in 3.5.2?


Is there a way to re-export an IBM Watson Analytics dataset and have it refresh rather than create new data? I haven’t refined the data in Watson, just testing after doing a TRIRIGA 3.5.2 upgrade. I exported a dataset from TRIRIGA, waited a day, exported again, and it shows as a second item in Watson.

This is an issue, because there are now discovery sets apparently pointing to the first data import, so the new data isn’t showing up. I don’t see a way to re-point the discovery set, and would prefer to not recreate after going into the new data. Am I overlooking something?

Unfortunately, there is no way for the TRIRIGA Connector for Watson Analytics to refresh or append to an existing dataset, because that capability was not available in the Watson Analytics API when we were developing the TRIRIGA Connector. It’s a good RFE candidate once supported by Watson Analytics and I encourage you to submit a request for enhancement (RFE) under “IBM TRIRIGA Platform”. Also, a good source of information is the Watson Analytics forum.

[Admin: This post is related to the 12.15.16 post about finding information about the IBM TRIRIGA Connector for Watson Analytics, the 11.15.16 post about the overview and demo, and the 07.09.16 post about combining IBM Watson Analytics and IoT with TRIRIGA. To see other related posts, search “Watson Analytics“.]

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Where can you find information about the IBM TRIRIGA Connector for Watson Analytics (CWA)?


You can use the IBM Watson Analytics service to discover patterns and meanings in your data. Watson Analytics guides data discovery and predictive analytics by using automatic visualizations and enables dashboard creation so that you can get insights and make informed decisions.

By using Watson Analytics, you can add and tune data, discover insights to help solve your business problems, and display your findings. You pair Watson Analytics with data from IBM TRIRIGA to explore your integrated workplace management processes and obtain new insights on your day to day operations. You use TRIRIGA Connector for Watson Analytics to export TRIRIGA data of your choice into Watson Analytics.

[Admin: This post is related to the 11.15.16 post about the overview and demo.]

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What are Siemens’ strategies for real estate decarbonisation?


Reducing the carbon footprint of buildings and enhancing sustainability are fast becoming business critical issues for institutional real estate investors. But in order to implement an effective green strategy to lower one’s carbon footprint, having access to detailed building performance data and analytics is vital. Siemens is able to achieve this to help investors enhance the value of their real estate portfolios…

Towards low carbon real estate

On average, energy consumption accounts for 40% of the lifecycle costs of buildings. For those wishing to optimise their real estate and demonstrate green credentials, and compliance with global standards, such as Europe’s Energy Efficiency Directive (EED) and LEED (Leadership in Energy & Environmental Design), commonly used in the US, Siemens is able to plot a path to greater transparency and performance…

The power of big data

To connect buildings and foster smart buildings, in February 2016, the Siemens Buildings Technologies Division partnered with IBM to maximise the potential of connected buildings and the data they create to empower real estate owners and operators to drive business results and meet building-related KPI’s. As a result, Siemens develops integral solutions with IBM‘s software suites Maximo (asset management) and TRIRIGA (workplace management) and envisages use cases to apply Watson IoT analytics. A cognitive building can anticipate, respond, and adapt…

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[Admin: As a thought-provoking counterpoint, the following piece reveals a mathematical flaw in current climate models that are supposed to predict the rate of global warming for every doubling of carbon dioxide (CO2) in the air.]

Carbon scare is over?

In December 2016, Lord Christopher Monckton reveals a breaking discovery which may prove the entire “climate change” scare is based on faulty mathematics. At the “Global-Warming; an Inconvenient Lie” conference in Phoenix, AZ, Lord Monckton covers in-depth the mathematical discovery his team has made and announces that these findings have been submitted for proper peer review…

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[Admin: At time index 3:29, the 4.5 K value is highlighted. But looking at Lord Monckton’s London presentation from September 2016, the 2.2 K should be highlighted instead. Basically, the flaw was taking the mid-value of T (at 3.0 K) on the y-axis, instead of taking the mid-value of F (at 0.485) on the x-axis. That’s what he means by “the wrong end of the curve” or the wrong axis. So by using the F of 0.485, the curve gives a much lower T of 2.2 K. Then 2.2 K should be highlighted.]

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IBM TRIRIGA Connector for Watson Analytics overview


Here’s an overview and demo of the IBM TRIRIGA Connector for Watson Analytics, which lets business professionals easily integrate their TRIRIGA building data directly into Watson Analytics. Presented by Paul Lacey, Offering Manager for IBM TRIRIGA.

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ValuD: Interview with Motors@Work


As “going green” becomes not only a trend but a necessity in our world today, it’s important we talk about energy conservation. Industrial consumption accounts for at least 42% of the global electricity consumption and a significant portion of that is wasted. With one third of such industrial consumption being used by electric motors, it goes without saying that energy conservation in electric motors is a must have. And that is exactly where Rod Ellsworth and his team at Motors@Work(TM) come into the picture…

VC: Good to know that Motors@Work(TM) has earned the trust and confidence of its clients! You recently announced that Motors@Work(TM) will be available as a SaaS solution in IBM Bluemix. Are there any specific reasons for you to choose IBM Bluemix as your Platform as a Service (PaaS) provider?

RE: Well, there are quite a few reasons for us to choose IBM Bluemix as our PaaS partner. For one, IBM currently happens to be one of the leading PaaS providers in the market today and is also the market leader in the Asset Performance Management sector. The way we look at it, Motors@Work(TM) energy intelligence has value on its own but our customers will get the extended benefit when our energy intelligence data is combined with historical data and live data streams from connected devices, sensors, and other IBM Watson IoT hub components. Moreover, the systematic integration of Motors@Work(TM) with Enterprise Asset Management (EAM) Applications will provide seamless business process integration for non-conformity mitigation and energy improvement opportunity execution. Additionally, this partnership will enable us to integrate our application with IBM’s Maximo Asset Management and TRIRIGA RE/FM applications, which again would be beneficial to our clients. We feel that this will be a win-win relationship for us as well as for our clients…

[Admin: This post is related to the 02.18.16 post about the ValuD partnership with Motors@Work.]

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How can you combine IBM Watson Analytics and IoT with TRIRIGA?


How can IBM Watson Analytics and IoT technology be combined with TRIRIGA to achieve real estate and facilities management goals? How can we leverage Watson Analytics and IoT technology to improve TRIRIGA capabilities?

[Admin: This post is related to the 04.24.15 post about unlocking TRIRIGA possibilities with Watson Analytics, the 03.23.15 post about connecting TRIRIGA to Watson Analytics, and the 06.05.16 featured post about the difference between Watson Analytics and cognitive IoT.]

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