BASSG, LLC. - Blog Mon, 22 May 2017 10:21:54 -0500 MYOB en-gb Milestone from BASSG with Edge Analytics Controllers by Anka labs and Ontrol R-ION & P-ION BASSG has just completed another controls engineering project that deploys edge analytics controllers (EACs) from Anka Labs ( and the open-source Sedona Framework for Building Management System (BMS) programming. This project pairs Anka EACs with Sedona room and field controllers from Ontrol. R-ION devices interface with P-ION universal... ]]> (controls4all) Blog Mon, 06 Feb 2017 13:21:04 -0600 BASSG has just open sourced one of its most popular software tools - Project Builder Plus (PB+) BASSG has just open sourced one of its most popular software tools - Project Builder Plus (PB+) , for free use and development by the community. The PB+ editor makes it easy to work on large trend data files from your desktop, adding Project-Haystack tagging and custom extensions in preparation for importing data into analytics and visualization ap... ]]> (controls4all) Blog Mon, 06 Feb 2017 13:02:23 -0600 Join BASSG at the Leading Edge Alper Uzmezler is the CEO and creative mind of BAS Services and Graphics, LLC (BASSG). This year BASSG introduced the Edge Analytics Controller, a hybrid fog-computing device that can serve as gateway and field DDC. It is known for developing groundbreaking applications for managing building data and creating custom UIs. BASSG's goal is to keep ide... ]]> (controls4all) Blog Tue, 04 Oct 2016 19:27:11 -0500 When Will Machine Learning Reach Smart Buildings? There is a Mount Olympus-like battle over Machine Learning dominance happening among Tech Titans. Smart Buildings and the IoT exist in the weather created by that battle. Google, Amazon and Facebook are in the midst of reinventing themselves as Machine Learning (ML) companies. In fact, they are in heated battle to be ML's #1 contender. ...

There is a Mount Olympus-like battle over Machine Learning dominance happening among Tech Titans. Smart Buildings and the IoT exist in the weather created by that battle.

Director of Data and Information Products for the Royal Institute of Chartered Surveyors

Google, Amazon and Facebook are in the midst of reinventing themselves as Machine Learning (ML) companies. In fact, they are in heated battle to be ML's #1 contender. They've recognized that ML is the edge that they need to be the best in advertising, cars, consumer marketplaces or whatever other business they'd like to enter in the future. ML algorithms have advantageous self-correcting behaviors that will be the best navigators of a digitized world. But, these come at the price of being more complex to understand and work with than, for example, rule-based analytics programs. And they require a continuous and ample supply of structured data to deliver any meaningful results. When Amazon Invests $35 Million Into Nest Competitor Ecobee, we can be sure that it wants access to contextual data from home owners to better compete with Google in ML. But how about all of the other software and hardware developers in the Smart Buildings, Smart Grid, and Smart City markets that are now touting their ML capabilities? Can they make the claims of an Amazon or Google? The Machine Learning Club actually has very high barriers to entry, in terms of the data, patience and deep pockets required. These factors make ML impractical for most Commercial & Industrial applications today, including Smart Building edge analytics. But that is no reason notto get started readying your buildings for Machine Learning right away. ML is coming and there is value to unleash just in being prepared. You can start by structuring all building design, construction, operations and maintenance activities around the concept of an IoT Platform that supports unobstructed data flow and simple feedback loops. The main building blocks will be edge controllers, cloud services and a library of powerful data analytics apps.

There is no technology more imperative to get right, right now, than data strategy. Dan Hughes, Director of Data and Information Products for the Royal Institute of Chartered Surveyors (RICS), just published this chart plotting what new technologies property managers should most concern themselves with and when. He too puts 'data' as the most important area of investment and attention. If you get this right, you'll be poised to leverage more futuristic tech like ML when the time is right.

Machine Learning Defined and Located

2016 Hype Cycle for Emerging Technologies

To plot where we are right now on Machine Learning's evolutionary curve, it is useful to remember how companies like Google, Facebook and Amazon took shape to begin with. Before you could have Google Spiders crawling the Worldwide Web, you needed to have the HTML language and a lot of people – ie, the Commons – structuring their information as HTML web pages. A big part of the Commons were product sellers, and Amazon trained its spiders on their particular structured data – product SKU's, prices, etc. Facebook focused on getting another segment of the Commons to give up their data to HTML publication and self-structuring. Today Facebook rules the friends & family social graph. In all cases, without a constant supply of data structured for free by users, there would have been no web for algorithms to crawl and no titan-sized companies emerging from the effort. Currently, the only part of the Commons structuring buildings-related data in HTML is the Project Haystack open source organization. It's a hard-working community but cannot be compared to the armies of online product sellers that embraced the Amazon marketplace app or to all the 'friends' on Facebook.In an article that bounced to the top of social news feeds tuned to Machine Learning this summer, The Business Implications of Machine Learning, Data Strategist Drew Breunig makes the point this way: "Machine Learning is only as good as its training data." He goes on to define training data as "Data which has been tagged, categorized, or otherwise sorted by humans." Though the author comes from the perspective of a data scientist focused on online advertising challenges, he lays out a universal framework for understanding the ML-transformed tech landscape of today and delves into the hardware, software and data implications. He explains the role of Reciprocal Data Applications (RDAs) –Facebook Photo, Amazon's Alexa, Google-Nest OS. He makes a good case for why there is no near-term risk that one of these titans of ML will swallow up the building optimization market soon. 1) There is not enough of the Commons creating training data to feed ML algorithms for optimizing typical commercial buildings. And 2) because each building needs to be uniquely modeled for ML, there is not enough money to be made in the effort.Published just on time to serve as illustration for this article, the Gartner Group has just released its 2016 Hype Cycle for Emerging Technologies. Machine Learning is at the very top of the curve, poised to dive into the Trough of Disillusionment.

Realm of the ML-Ready

A company with the size and might of Google is not bounded by others' realities. In July, it released a story about how it is using Machine Learning algorithms to cut data center energy bills. Results from its first experiments with this include a 40 percent reduction in the amount of electricity needed for cooling. This is a real case study of ML success in HVAC. But, it is also a statement about why the approach is so out of reach for most Smart-Building app developers. Google paid $600M to buy the Artificial Intelligence (AI) company that designed and led this project, and it expects to save hundreds of millions of dollars in energy costs yearly. Moreover, don't expect Google to be commercializing the ML algorithms it used to achieve this success soon. Better Data Center Infrastructure Management (DCIM) is another way that it competes with Amazon as a Cloud Host, so it will protect that intellectual property. However, in the future, it will likely be getting into the business of Algorithms as a Service.IBM is another big tech company positioning itself for leadership in ML. The IBM Watson IoT Platform is the flagship for this effort. (You will find 'IoT Platforms' category label about seven beads behind ML on the Gartner hype cycle.) The showcase examples of IBM Watson IoT are like this oil rig case study. The big money in natural resource extraction was sufficient motivation to pay for the pioneering data science. Also, in an industrial plant setting like an oil rig where just about all operations are already mission-critical and unpredictable humans inputs have been minimized, there are fewer what-if possibilities. As with the Google data center, this is fertile ground for ML. It's also on the other end of the spectrum from a modern multi-story corporate or institutional building.The takeaway lessons from these first case studies are that you should ask three questions before considering ML for buildings optimization:

  • How many known variables? (Fewer is better.)
  • What is the likelihood that some unknown variable, or black swan event, has power to null results? (Low likelihood needed.)
  • Is there sufficient money to be made to make the investment in ML worthwhile? (Big money necessary today.)

Most Smart Buildings applications wouldn't be above the bar set by these practical criteria. So the building automation industry at large isn't really ready for Google, Amazon, or IBM-style ML today. So don't look to them for a Smart Buildings Reciprocal Data Application (RDA) that will spin up like a tornado inspiring knowledgeable people to donate training data. 

Readying Your Buildings for ML

Recognizing that it is the lack of structured HTML data – ie, a web for an algorithmic spider to crawl – that is holding back ML in Smart Buildings, the question is how will that web be built. Scott Muench of J2 Innovations in his July article, The Strategy and Payoffs of Meta-Data Tagging, explains all the benefits possible when a building operator insists on open, industry-standard systematic tagging and data modeling. These are harvestable even before building operators consider the head-start they will have in the deployment of ML. These advantages will encourage the construction of a communal semantic web of buildings. All the buildings professions will start to contribute. According to Muench, "Requirements will be written into specifications to the effect that all vendors of equipment, meters, other building-connected devices and software adhere to standard tagging and modeling conventions. Every device or piece of software will be delivered with a zip file containing Haystack-compliant data models." BASSG's Edge Analytics Controller is the first BAS edge device that already has Haystack functionality built into its software stack.Once whole buildings, even whole cities, are modeled in a semantic web system like Project Haystack, buildings data scientists (a new profession) can take over, knowing the training data needed to support ML algorithms is there. Like successful pattern-recognition analytics today–for example, SkySpark from SkyFoundry—ML spiders will be built to look for situations with known patterns that can be expressed as variables in a complex algorithm. They will collect the necessary data, grabbing all the points involved from the zone, multizone, whole building, whole campus levels—whatever the scope involves. As long as they are working on current reliable data, they will return impressive results.You will know when Machine Learning becomes a reality in your building when computers, rather than engineers, start making decisions. For example, today data analytics programs regularly crunch building operational data looking for faults and anomalies and generating alerts and alarms. An engineer looks at that data, makes some decisions about it, and possibly takes some actions, like replacing a chiller. In the era of ML, you would start with a question "Do we need a new chiller?" Then you would give the algorithm the data set and it will tell you what to do. As Google and many other data center operators have already found out, the ML approach to infrastructure management pays off. They are constantly adding and swapping servers with changing power, thermal, downtime risk and cost implications. DCIM is definitely an early market for the type of decision support that ML algorithms provide.

Building equipment manufacturers are also positioned to take early advantage of ML. There has been a growing trend to incorporate sensors and telematics (in other words, the IoT) into maintenance and service contracts. Collecting and sending operational data to factory technicians for remote monitoring improves preventive maintenance, helps to avoid warranty disputes, and opens the door to more flexible pay-for-performance pricing models. Applying ML algorithms to the collective data for a particular make and model AHU, for example, is a natural next step. The investment in algorithm development makes financial sense when it can optimize thousands of AHUs.

As previously stated, ML leaders will soon be getting into the Algorithms as a Service business. Pivots and advancements happen fast in the modern-day Mount Olympus settings like Google and Amazon research environments. The Artificial Intelligence (AI) unit known as Deep Mind that led the Google data center ML project has already been merged into TensorFlow, an open source software library for Machine Learning launched and curated by the Google Brain team. Resources like TensorFlow will make access to the latest AI methods and data science talent more affordable. Of course, to take advantage of these services for building optimization, you will still need to start with fully tagged building assets. Again, owner/operators that get started today on this will be among the first to be able to leverage Machine Learning to gain competitive advantage in their own businesses tomorrow. 

]]> (controls4all) Blog Thu, 01 Sep 2016 19:45:17 -0500
Visualytik version .3 is being released in a week What is Visualytik? Visualytik stands for visual + analytics.  Analytics visual generation enhanced with visualytik Raynor Controls, 2016 ​There are 3 parts of visualytik. ​Drag and drop GUI items Block programming  Page manager It is built-on SkySpark framework and utilizes haystack in its core. Live view of the dashboard ​Drag and Drop ... ]]> (controls4all) Blog Sun, 27 Mar 2016 23:28:14 -0500 Edge Analytics Controllers Edge devices now have the intelligence and data storage they need for local analytics and machine decision-making. They'll soon be the thing that the rest of the BAS universe revolves around.When people say that we are living in the post-PC era they mean that the personal computer is being eclipsed as the center of the IT universe by the smartphone...

When people say that we are living in the post-PC era they mean that the personal computer is being eclipsed as the center of the IT universe by the smartphone. Operations technology is experiencing a similar reordering. In this new era of the Internet of Things (IoT), compute resources equivalent to a PC or smartphone are being integrated into all sorts of equipment and devices. For commercial buildings, a new category of IoT device is emerging—the Energy Analytics Controller (EAC). Smart building applications development should revolve around the enormous possibilities of these edge devices.

What is an edge device?

Anyone designing an IoT architecture must decide which tasks are best performed locally by a device at the network's edge versus remotely by a cloud-hosted application. Within the IT world, an edge device is defined as a gateway or global controller. Within the building automation world, a direct digital controller (DDC) can be considered an edge controller. Likewise, a global controller is an edge controller. Physically, the network's edge might be integrated into roof-top equipment, solar arrays, utility-owned equipment, data center infrastructure, etc. The EAC marks a new generation of edge-device in that they will come with tagged, preconfigured apps to automate the workloads typical at these edge locations.

One of the most revolutionary aspects of having robust compute resources at the DDC level is that edge devices like energy analytics controllers can do analytics processing of large data sets. Application developers are challenged to make the most of this new capability. The Buildings-IoT represents an opportunity to radically rethink the software architectures that define core workflows such as detecting and diagnosing faults in equipment, responding to occupant hot/cold calls, shifting energy loads to participate in demand response programs, and performing other building operations management tasks. Energy analytics controllers are capable of high-speed handling of the work involved in trending data, adding semantic tagging and generating analytics. Doing these tasks locally and sharing the results among other edge devices opens the path to a host of new applications.

The basic resources that an energy analytics controller should integrate include a powerful processor, on-board memory, flash storage and IP connectivity. The open Sedona Framework is the type of real-time controls engine that works well in a software architecture built to support EAC devices at the edge. Essentially, app assembly happens here. Using easy-to-learn graphical block programming methods, solution developers can define desired inputs and outputs to EACs. Tridium has opened the Sedona Framework to the public with an academic free license and it has self-sustaining community support. 

Bringing the cloud to the edge

Another trend is to run building services on an IP backbone, bringing high-speed Ethernet connectivity all the way to EAC devices. This provides unprecedented capacity to store and compute data on the edge. A Smart Building System Integrator can use this broadband capacity and the EAC's resources to fundamentally change how equipment is controlled. At this point, it becomes practical to design solutions that involve:

  • Importing data from linked processes
  • Enforcing machine rules based upon reported run-time and occupant comfort issues
  • Bringing tenant satisfaction information from a web user interface or installed touch-screen device
  • Visualizing real-time and historical data
  • Interfacing with HTML5 dashboards that permit easy block programming with tags
  • Generating visualizations and analytics based on controls logic results

How it Works

Edge Analytics Controller 916 by Anka Labs

Even with all the power of IP-enabled EAC edge devices, finding operational anomalies is a complex task. It starts with transferring streams of data into a historian, or high-speed database. This transfer of data is the preliminary requirement. Next the raw data needs to be structured in preparation for analysis. This step is being made easier by semantic tagging systems that enable the definition of models that are self-describing. The semantic system can be integrated with the analytics program for greater efficiency. BASSG utilizes the open-source Project-Haystack standard for semantic modeling. It also uses the Skyspark® Analytics engine from Skyfoundry in its EAC architectures. For visualizations and dashboards, BASSG uses it own branded software, Visualytik.

A Project-Haystack software stack is built upon multiple technologies such as the text-based open source file format Zinc. Zinc allows semantic tag definitions called markers. Multiple markers combine together to define what a BMS point means and does. There is also a web-services layer of the stack for querying data within the database.

Once a data management architecture like this is set up the power of the EAC can be brought forward. For example, in a data center optimization scenario, an EAC could be used for cooling optimization. The goal is to keep the environment sufficiently cool to not risk overheating processors and server failures, while not wastefully over-cooling the space. This balance is largely a function of the amount of heat that servers are generating, and this is highly dependent on their processing load. An EAC-enabled workflow set up for this challenge would have the edge devices capturing real-time CPU readings and calculating a cooling load value from this data. The EAC would then feed this value to the CRAC unit as a parameter. In response, the control system would deliver more or less cooling. All this would happen before the space was allowed to heat up to the point that a room thermostat detected the change in temperature. This proactive approach to cooling would decrease risk of server failure due to overheating, improving the reliability of the data center overall. The availability of EAC's at attractive price/performance points would make this a viable approach for data center operators.

Stay Tuned

EACs are a powerful addition to the smart building system integrator's tool case. Going forward, EACs will serve in the core functions of:

    • Data (Transfer In-Out) with semantic modeling
    • Analytics
    • Visualization on the web
    • Tuning Real-time and historical control algorithms

      As described in the data center optimization example above, tagged, preconfigured apps to run on EACs will be the trend in the future. Real-time control, analytics and visuals will never be the same again. EACs are the way control will be done in the 21st Century.

      ]]> (controls4all) Blog Wed, 09 Mar 2016 20:02:42 -0600
      Edge Analytics Controllers Edge devices now have the intelligence and data storage they need for local analytics and machine decision-making. They'll soon be the thing that the rest of the BAS universe revolves around. When people say that we are living in the post-PC era they mean that the personal computer is being eclipsed as the center of the IT universe by the smartphon... ]]> (controls4all) Blog Sat, 05 Mar 2016 21:49:33 -0600 EnergyDVR Dashboard @ Turkish Airlines Successful completion of Turkish Airlines DashboardEnergyDVR Dashboard has been successfully setup by our dealer En-Ko in TurkeySimulation building personel will save hours from daily workPower distribution statusPower generator statusPower and energy chartsBattery bank statuses{gallery}TurkishAirLinesBlog{/gallery} ]]> (controls4all) Blog Sat, 07 Feb 2015 08:43:09 -0600 Sustainability efforts of a leader SustainabilityMustafa Kemal Ataturk

      Today is his 76th year of his passing, commemorate him with respect.

      Sustainability in the year of 30’s was unheard of within the leaders of the Free World.

      We were thought about the sustainability with exemplary projects during those years.

      Moved a home on skids, just to save a single tree.Created “Ideal Republic Village” for sustainabilityCreated Ataturk Forest Farm on a dry landWalking Mansion

      There are only a few major attractions in Yalova that are a must see for tourists and the Yuruyen Kosk is definitely one. This Ottoman-Turkish style mansion was said to have been used by Ataturk, the founder of modern Turkey, during his visits to the city. There is a legend connected to this mansion and it is often referred to as the “walking house.” The mansion has earned the name because as legend goes Ataturk thought that the mansion was too near to a spectacular tree, and as he did not want to obscure or cut down the tree he ordered the mansion to walk.Yuruyen Kosk

      Mustafa Kemal Ataturk first visited Yalova in 1927. He had specifically chosen this as his destination as he wanted to make use of the local thermal facilities. Whilst he was staying in this area he stayed in Baltaci Farm which was a wooden pavilion next to the thermal bath.

      ]]> (controls4all) Blog Mon, 10 Nov 2014 00:00:00 -0600
      Discounts, Celebrating SkySpark EU meeting, Greenbuild and Verge Contact us 1-512-540-3010 to receive your discount## We will be attending :- GreenBuild next week stop by our booth.- Following week, we will be at Verge conference.Hope to see you there. ]]> (controls4all) Trade Shows Thu, 16 Oct 2014 00:00:00 -0500 Energy Dashboard Information is Key Feature of New EnergyDVR Product from BASSG The Energy Data Visualization Recorder Allows Users to See How Their System was Functioning at Any Time During its Use


      BAS Services & Graphics, LLC, an energy management product and services company, is proud to announce the latest addition to their suite of energy management products. Called the EnergyDVR or “Energy Data Visualization Recorder,” it is an analytical visualization utility that lets users see how their system was functioning at any point in its history.

      More than just graphs and numbers, the EnergyDVR shows people graphically how all of the energy dashboard data looked at any given time period they specify. The way it works is simple: it gives people historical playback data in the format most easily understood, just like a graphics interface. In fact, it is the same graphics just not in “live mode” but in “history mode.”

      The EnergyDVR is available now as an add-on package to the BASSG PX-Dashboard and can be loaded right into existing JACE and Supervisor front-ends. DVR functionality can also connect to multiple SkySpark by SkyFoundry instances. DVR feature can be switched between local Niagara station—which is offered by the Tridium company—and remote SkySpark instance. The EnergyDVR starts from $150 for Jace 3 and equivalent and can be purchased through the BASSG shopping cart system.

      ]]> (controls4all) Blog Mon, 22 Sep 2014 00:00:00 -0500 is offical site for shortlinks of BAS Services & Graphics, LLC.

      If you visit site, It will bring up a table of links of BASSG.

      You may filter based on column.

      Filter globally on all text.

      Thank you for being a valued customer of BAS Services & Graphics, LLC.

      Don’t forget to bookmark the link.

      ]]> (controls4all) Blog Sun, 20 Apr 2014 22:26:57 -0500
      Project Builder Plus meets Project Haystack Project Builder Plus is a workflow enhancement software tool for projects that integrate control system and sensor data. Features include:

      Creating Duplicating Templating - How Templating Works? Querying - Query Record Tree Exploring - Tree views Exporting - Export to Zinc Importing - Import from Nhaystack Mapping 7 - Map Obix

      Project Builder + streamlines data integration and database creation to save time andmoney during implementation of data-oriented projects (analytics, graphical dashboards).

      Simplify Learning Haystack model visually - Video Generate haystack definitions offline on any computer (PC / Mac) Export these to a zinc file format - Video Generate definitions from existing xml through web services Niagara Px to Haystack hierarchy and tagging - Video Import definitions automatically from multiple NHaystack Connections -Video Mapping Obix MapObix ImportFromPx NHaystack NhaystackImport NHaystackMapping CSV (On Roadmap) Json (On Roadmap) SQL Simplified (On Roadmap) Video

      Feel free to watch the videos, they are marked with number with its respective subject.

      If you have any questions or want to say hi, you may contact me or 954 618-8070.

      ]]> (controls4all) Blog Fri, 07 Mar 2014 16:00:41 -0600
      BASSG's take on Project Haystack

      Advancements in building automation software (BAS) have made it easy to control lighting, regulate HVAC systems, and operate equipment via computers or mobile devices.  Modern BAS collects massive amounts of data that is greatly underutilized because it is rarely analyzed to its full potential.  The main hindrance to more effective analysis centers on the need for a standardized language to make this valuable data easily shared and accessible between different applications.  To address this concern, Project Haystack was formulated to create a forum where leaders in the industry could assimilate ideas for a common language to describe the data produced by today’s equipment systems.

      BAS Services & Graphics, LLC (BASSG) is a staunch supporter of Project Haystack.  As an avid participator in the initiative, BASSG is helping to contrive a new language for the benefit of the industry.  BASSG also understands that a standardized language is the next step in frontier of building automation and will improve building operations in a number of ways.

      Smart BAS Technologies

      As companies, communities, and cities embrace energy conservation through advanced technologies like smart grids, the massive volumes of data collected will require rapid analyzations to optimize energy usage.  In theory, an advanced smart grid could analyze data from thousands of BAS systems and optimize energy usages on a city scale.   A system capable of doing so could save millions while significantly decreasing energy waste.  BASSG is assisting the pioneering effort to create a language that will make it possible to develop these types of truly intelligent systems.

      Reduction in BAS Labor Costs

      The conceptual models built within BAS systems are often the effort of vigorous manual labor.  A standardized language for describing our systems and equipment will make it possible for standard automated processes to replace labor-intensive manual approaches.  Such automation is useful in two significant ways.  First, different applications can analyze equipment system models and present them to users accurately.  Second, this enables the creation of advanced intelligent automation systems that can recognize equipment systems and their data, assess external and internal conditions, and improve building operations on a continuous basis.  Automated models would greatly reduce labor costs, which will make building automation more cost effective for building owners of all types.

      By actively engaging in Project Haystack BASSG is helping to shape the future of building automation.

      ]]> (controls4all) Blog Mon, 17 Feb 2014 23:07:08 -0600
      Energy Analytics Company BASSG Collaborates with Leafclick to Release New Product Called Sparko Austin, TX -- (SBWIRE) -- 03/25/2013 -- BASSG, a company that specializes in energy analytics and management products, recently worked together with leafclick to launch a product called Sparko, which fits into the family of applications that make the system SkySpark more comfortable than ever before. The Sparko application is a plug-in for SkySpark - which was created by theSkyFoundry company - that takes sparks that are generated by a company’s control system and communicates them directly to a Kayako Resolve ticket management database using the REST API for integration. 

      For people who manage a control system, becoming familiar with SkySpark is crucial. It has the remarkable ability to turn control system data into money saving opportunities. SkySpark is an analytical engine that uses information from a control system database to identify deficiencies in operation and anomalies that potentially waste energy and resources. These opportunities are otherwise known as sparks. Identifying and resolving sparks can also reduce maintenance costs and prolong equipment life cycles.

      Traditionally, the problem with having any type of system that helps identify opportunities is that people have had to create an action to do something about it, or the opportunity is wasted. Sparko gets a company’s team into the action by generating an automated work ticket and distributing it to all of the pertinent parties instantaneously. It gets the group on task to take advantage of savings opportunities.

      The Sparko application integrates into Kayako Resolve and creates a point of collaboration for everyone who is involved to gather around and attack an issue from all sides. The control tech, the facility manager, the energy manager, the design engineer and others can all be notified immediately and begin working to remedy the situation. 

      The Kayako Resolve database keeps track of progress on all sparks organized by equipment or subject matter as they are brought in by Sparko. Opportunities can be split or merged as needed based on commonality or resources. Kayako Resolve has on optional asset management plug-in solution that can be added to give users a complete work order system for all of their monitored equipment. There are also mobile and iPad applications that allow communication and collaboration from just about anywhere. Workers can identify and respond to sparks from a mobile phone with just a simple swipe and click.

      The combination of SkySpark, Sparko and Kayako gives users the capabilities of similar facility management and ticket management systems that can cost thousands more and offer less integration capability. 

      Sparko is the bridge that brings sparks from opportunity to resolution, identifying sparks and porting them over to work tickets for all to evaluate and proactively resolve. Sparko reduces the need to constantly monitor a system and brings anomalies to the forefront faster and completely automated, based on the rules that are created in SkySpark. With Sparko creating tickets for opportunities before they become a problem, the work team becomes proactive instead of reactive and increases their efficiency.

      System requirements – Same as SkySpark
      SkySpark versions supported - 1.0.39 , 2.0.2 

      About leafclick s. r. o.
      Leafclick s. r. o. is a technological company that develops wide range of software solutions including ones built upon SkySpark platform. Since its foundation leafclick has worked closely with BASSG in the field of optimization and energy savings. Quick orientation in its segment, operational problem solving, the desire to continuously learn - these skills are the driving force of leafclick. The company head office is located in Prague, Czech Republic. For more information, contact Jiri Niznansky at 

      About BASSG, LLC
      BASSG, LLC is an energy management product and services company that provides innovative products to support integrators and end-users of energy management products. The company creates highly intuitive interface products and provides state of the art components for implementing a complete and highly evolved energy management solution. The company headquarters are in Austin, Texas; with an additional office in Istanbul, Turkey they serve customers all around the world. For more information, please visit

      ]]> (controls4all) Blog Mon, 01 Apr 2013 17:02:57 -0500