Locus’ cloud-based environmental data management software solutions provide full support for environmental automation and real-time monitoring, so you can monitor and access your control systems from anywhere.

IoT is considered one of the fastest growing trends in technology and has a potentially huge impact to automate how we manage water quality, air emissions and other key environmental performance indicators for data monitoring.

In this white paper, we focus on how EHS programs can benefit from integration and interoperability of a multi-tenant cloud platform and Internet of Things (IoT) platforms for managing, organizing, and monitoring the structured and unstructured data coming from various different sources. Once in the platform, a centralized data repository is created that is suitable for analyzing the key environmental indicators for management, sustainability, and environmental compliance.

Locus Technologies founder and CEO Neno Duplan provides a wealth of experience on water quality in the cloud. Neno began cloud-based data work before any of us knew what the cloud even was. He does a great job explaining the steps needed to undertake and the significant benefits of a cloud-based digital transformation, and much more!

Neno Duplan is founder and CEO of Locus Technologies, a Silicon Valley-based environmental software company founded in 1997. Locus evolved from his work as a research associate at Carnegie Mellon in the 1980s, where he developed the first prototype system for environmental information management. This early work led to the development of numerous databases at some of the nation’s largest environmental sites, and ultimately, to the formation of Locus in 1997.

Mr. Duplan recently sat down with Environmental Business Journal to discuss a myriad of topics relating to technology in the environmental industry such as Artificial Intelligence, Blockchain, Multi-tenancy, IoT, and much more.

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Click here to learn more and purchase the full EBJ Vol XXXIII No 5&6: Environmental Industry Outlook 2020-2021

Integration with other systems, whether on-premises or in the cloud, has become a key wishlist item for many EHS software buyers.  It allows you to take advantage of other tools used by your organization (or available from third parties) to simplify processes, access information, and enhance communication, both internally and externally.

“49% of EHS software buyers are unhappy with their current solution’s poor integration with internal IT systems.”

NAEM 2017 EHS & Sustainability Software Buyer’s Guide

This blog will take a look at some common types of integrations we most hear about when talking with EHS professionals.

  • Integrate on-premises systems with cloud EHS software to provide a seamless business process
  • Integrate with identity providers to enable single sign-on
  • Integrate with public API (like EPA or regulatory information providers) for data submittal or private commercial APIs for proprietary content
  • Integrate with multiple sources for consolidation and review of disparate data sources (portal integrations)

Integration with on-premises systems and cloud EHS software

Many potential software buyers want to integrate an existing on-premises system with a Software as a Service (SaaS) system. The SaaS integration advantage is accessing information in existing systems without the additional user burden of using multiple software systems, making it easier to perform parts of a unified business process.

A good example is creating a cloud system that integrates with an onsite Enterprise Resource Planning (ERP) system such as SAP.  Many business processes must connect external vendors with internal resources to track work, scope, invoices, and payment.  Cloud systems are ideal when external vendors need internal interactions.

Locus Technologies Vendor Integration

In this example, previously, vendors sent invoices as email attachments, then had to be manually entered into the owner’s ERP system.  The vendors had zero visibility into the processing status of the invoice, thus slowing down the flow of information between the owner and vendors.  The owner wanted to create a single view of the contracts, vendors, the approved budgets, and the payment approval status to streamline the process and enhance transparency.

To meet the customer’s enhancement request of a “Single View” data had to be integrated securely between the on-premises secure ERP system and the cloud system. First, the owner established a secure web service API.  The cloud system authenticates and consumes the API over an encrypted connection to integrate the process.  Maintaining security for all parties, and application users have full access to the specific data they needed to complete their business flow.

One caveat to this type of solution is that the owner’s IT personnel are often involved in establishing a pathway to the internal data and gaining internal approvals for this to happen.  Therefore, if you are considering integration with internal systems, check with your internal resources to make sure they can accomplish your goals while adhering to your corporate IT security policies.

Integration with identity providers

Many companies request single sign-on (SSO) for their users when accessing a third-party cloud software.  This functionality can be a key purchase criterion for selecting a software vendor.  SSO integration termed “integration with identity providers,” is especially important for large enterprises and many geographically distributed users.

Using SSO, company employees authenticate in their own employee portal.  When an employee clicks on a link to the software provider, they are taken to the provider’s website and presented with the correct information based on her authorization, without having to log in again.

Locus Technologies Integration with Identity Providers

One approach to providing this functionality is to use Security Assertion Markup Language (SAML) assertion through integration with the company’s identity provider (IdP). In a typical use case, the vendor software maintains the user identities and permissions for every employee at the company needing access to the EHS software.  When an employee of the company accesses the vendor’s SaaS applications, the SaaS sends an authentication request to the company’s IdP at this point he IdP authenticates the user and sends a SAML response.  Allowing the user access the relevant parts of the vendor’s SaaS software.

The user experience is a seamless workflow and one less set of credentials to manage.  It also provides a method to simply and quickly remove users when they leave the company or no longer need access to the software.  Once removed from the company authorization, access to the external software is also removed.

Integration with public/private APIs

Government agencies and other public/private entities are increasingly delivering services or requiring data submissions via publicly available APIs (Application Programming Interfaces).  API’s help both parties by significantly streamlining data submission (such as submitting hazardous waste manifests to EPA).  More importantly, electronic data submissions all but eliminate the tedium and the likelihood of errors in manual and repetitive data entry.  However, the benefits of API data exchange impact users only if their software is capable of making the connection and sending the information safely and accurately to the intended recipient.

Locus Technologies Integration with Government API

EPA’s public REST API for submitting GHG emissions reports

One example where APIs are extremely useful is in the submitting annual GHG (greenhouse gas) emissions to EPA.  Previously, GHG regulatory report submission was a manual process on the EPA website.  Data was typically calculated in spreadsheets for multiple sites and manually entered into the EPA website.  This manual process was both slow and error prone.

To streamline the regulatory reporting process, EPA introduced an option to upload files that are formatted per EPA specifications.  The specification allows combined data from each reporting site (for multi-site facilities) to be submitted as a single consolidated XML file.

The software can provide reporting tools that make it easy to run multiple reports and combine results in a single XML, which is designed to be suitable for submission to EPA.  In the future, when EPA provides an API, the process will become even more streamlined for data owners, and the upload portion of the process can be eliminated.

EPAs e-Manifest system has recently gone live and is another great example of integration with a public API.  Automating submission of hazardous waste manifests will be a huge time saver for all entities that are required to ship waste.  The new system will also finally put to rest the clunky dot matrix printers that are kept around only to print out these manifests.  For this type of integration, look for built-in tools to configure automatic submission of manifests with EPA’s public REST API web service.  Because this is new (July 2018), expect some trial and error on the first submissions, but after the integration is worked out, it will be a game-changer for companies required to ship and transport waste.

eManifest website

GIS integrations for visualizing site and facility data

Mapping and geographic information systems are another popular public API integration option for EHS software. Using Esri (a leading geographic information system) public APIs, EHS software can validate location data for address formatting and accuracy across a range of applications.  This type of integration is typically easy to configure and you just need to create the business rules that integrate with Esri APIs to check, format, and store the correct address and geo-coordinates for locations.

Google Maps APIs can be used to show relevant maps of sites or facilities and overlay useful information like terrain, demographics, or traffic to make the EHS data more meaningful and understandable. Similarly, devices with GPS tracking can be visualized in dashboards to see current sampling locations or inspection locations in a map view.  These types of integrations are very familiar to most software users and are easy to configure as most sources of information are readily available publicly and come with well documented API information.

Intellus GIS screenshot of tritium concentrations near LANL Los Alamos, NM

Content services integrations provide up-to-date regulatory notifications

From a private API perspective, consider content management services like RegScan and Specialty Technical Publications (STP).  Using services like this, companies can connect with third-party content providers to get information about the latest important environmental regulations delivered within their software application. This is a great concept as no software vendor can excel at all dynamic regulatory programs, so it makes sense to purchase the information from providers who specifically focus on certain types of content.  Another example is product regulatory compliance or online Material Safety Data Sheets (MSDS).

Any modern online specialty knowledge vendor will be able to supply an API for accessing and integrating their service information into an existing modern EHS software solution.  This means EHS software users can access articles that provide clear analysis on evolving environmental regulations or other topics of targeted interest with the content managed by the content provider.  Additional features may also be available from the content vendor such as alerts or notifications so content consumers can stay up to date with changes.  Through robust integration via public or private API, software vendors can provide enhanced content to users well beyond what the software vendor natively supplies.

Two major advantages of public and private APIs is that no permissions are needed to access the information, and that users have immediate access to current and reliable content at all times.  Effective integrations can reduce the time needed to research related information from other sources and eliminate many manual errors by having applications connect directly to each other.

Locus Technologies Integration Notifications

Portal integrations

Another common integration request is “portal integration”, or the melding of various streaming data sources (such as “big data” or IoT data) into a single system to enable better data analysis and insight.  For example, many companies have multiple continuous monitoring systems (CEMS) that generate huge amounts of data at frequent intervals.  With such huge volumes of data, it is hard to review and take action without condensing the information into an understandable format.

A modern SaaS platform with built-in integration tools is essential to bring various data sources together and display the information in a meaningful way.  Look for dashboards designed to handle this type of data that provide a way to integrate data from different data sources into a single unified view that is easier to interpret. Look for tools that make it easy to combine and present data using different types of graphical charts and as GIS maps.

Like integrations with on-premises systems, system owners will need to be involved in setting up the integration, and software collecting the streaming data will need to be sophisticated enough to be readable by modern systems.  If you’re using legacy data collection systems such as SCADA in your organization, you can integrate with those systems as well, avoiding the need for costly hardware upgrades.  However, its best to check with the system owners to ensure their systems are able to integrate before you start your EHS system planning.

Locus Platform Automation Dashboard

Careful planning to ensure integration success

With all the advances in software platforms and commercial data sources providing enhanced linkage to data that was previously unavailable or behind firewalls, EHS software customers have a lot to consider when evaluating options.  In the last several years, software integration has become a hot topic and something most EHS departments are at least talking about.  If you’re evaluating EHS software solutions, you would be wise to add one or more of these integration capabilities to your “wish list” for any potential vendor solution.

Consider the exact information you want to bring into your EHS software, the quality of the information you want to consume, and the reliability of the source.

Also, remember that internal and external data providers may upgrade or change over time.  For that reason, the ease and reliability of integration is an important parameter to consider.

Locus Technologies Integration Planning

There are many clear benefits to taking advantage of modern integrated software tools wherever you can in your EHS processes.  Even if some integrations are only optional for your needs, consider the benefit to your organization in simplifying your EHS software implementation, maximizing other available resources, and improving the reliability and accuracy of data sources driving your EHS decisions.

Integrations are sometimes initially perceived as an optional feature, but you should consider making it a requirement for your EHS software based on these benefits. Moreover, as new integration tools increasingly become available, you’ll find more value out of having a system that can use them to their full advantage.


NAEM recently published this blog as a part of their Green Tie series. Read it here.

Interested in learning more about integration? We recently published a detailed white paper on integrated systems for EHS software. It is available as a free download.

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Let’s look back on the most exciting new features and changes made in EIM, Locus’ environmental data management software, during 2019!

1. Migration to AWS Cloud

In August, Locus migrated EIM into the Amazon Web Services (AWS) cloud. EIM already had superior security, reliability, and performance in the Locus Cloud. The move to AWS improves on those metrics and allows Locus to leverage AWS specific tools that handle big data, blockchain, machine learning, and data analytics. Furthermore, AWS is scalable, which means EIM can better handle demand during peak usage periods. The move to AWS helps ensure that EIM remains the world’s leading water quality management software.

Infographic: 6 Benefits of EHS on AWS

2. SSO Login

EIM now supports Single Sign-On (SSO), allowing users to access EIM using their corporate authentication provider. SSO is a popular security mechanism for many corporations. With SSO, one single login allows access to multiple applications, which simplifies username and password management and reduces the number of potential targets for malicious hacking of user credentials. Using SSO with EIM requires a one-time configuration to allow EIM to communicate with a customer’s SSO provider.

Locus Single Sign On (SSO)

3. GIS+ Data Callouts

The Locus GIS+ solution now supports creating data callouts, which are location-specific crosstab reports listing analytical, groundwater, or field readings. A user first creates a data callout template using a drag-and-drop interface in the EIM enhanced formatted reports module. The template can include rules to control data formatting (for example, action limit exceedances can be shown in red text). When the user runs the template for a specific set of locations, EIM displays the callouts in the GIS+ as a set of draggable boxes. The user can finalize the callouts in the GIS+ print view and then send the resulting map to a printer or export the map to a PDF file.

Locus GIS+ Data Callouts

4. EIM One

For customers who don’t require the full EIM package, Locus now offers EIM One, which gives the ability to customize EIM functionality. Every EIM One purchase comes with EIM core features: locations and samples; analytical and field results; EDD loading; basic data views; and action limit exceedance reports. The customer can then purchase add-on packages to get just the functionality desired–for example a customer with DMR requirements may purchase the Subsurface and Regulatory Reporting packages. EIM One provides customers with a range of pricing options to get the perfect fit for their data management needs.

EIM One Packages

5. IoT data support

EIM can now be configured to accept data from IoT (internet of things) streaming devices. Locus must do a one-time connection between EIM and the customer’s IoT streaming application; the customer can then use EIM to define the devices and data fields to capture. EIM can accept data from multiple devices every second. Once the data values are in EIM, they can be exported using the Expert Query tool. From there, values can be shown on the GIS+ map if desired. The GIS+ Time Slider automation feature has also been updated to handle IoT data by allowing the time slider to use hours, minutes, and seconds as the time intervals.

Locus IoT Data

6. CIWQS and NCDEQ exports

EIM currently supports several dozen regulatory agency export formats. In 2019, Locus added two more exports for CIWQS (California Integrated Water Quality System Project) and the NCDEQ (North Carolina Department of Environmental Quality). Locus continues to add more formats so customers can meet their reporting requirements.

CIWQS and NCDEQ Exports

7. Improved Water Utility reporting

EIM is the world’s leading water quality management software, and has been used since 1999 by many Fortune 500 companies, water utilities, and the US Government. Locus added two key reports to EIM for Water in 2019 to further support water quality reporting. The first new report returns chlorine averages, ranges, and counts. The second new report supports the US EPA’s Lead and Copper rule and includes a charting option. Locus will continue to enhance EIM for Water by releasing the 2019 updates for the Consumer Confidence Report in January 2020.

Locus Water Utility Reporting

8. Improved Non-Analytical Views

Locus continues to upgrade and improve the EIM user interface and user experience. The most noticeable change in 2019 was the overhaul of the Non-analytical Views pages in EIM, which support data exports for locations, samples, field readings, groundwater levels, and subsurface information. Roughly 25 separate pages were combined into one page that supports all these data views. Users are directed through a series of filter selections that culminate in a grid of results. The new page improves usability and provides one centralized place for these data reports. Locus plans to upgrade the Analytical Views in the same way in 2020.

Non-analytical views in Locus EIM

9. EIM search box

To help customers find the correct EIM menu function, Locus added a search box at the top right of EIM. The search box returns any menu items that match the user’s entered search term. In 2020, Locus will expand this search box to return matching help file documents and EDD error help, as well as searches for synonyms of menu items.

Locus EIM Quick Search

10. Historical data reporting in EDD loading

The EIM EDD loader now has a new “View history” option for viewing previously loaded data for the locations and parameters in the EDD. This function lets users put data in the EDD holding table into proper historical context. Users can check for any unexpected increases in parameter concentrations as well as new maximum values for a given location and parameter.

Historical Data in Locus EIM

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    At Locus Technologies, we’re always looking for innovative ways to help water users better utilize their data. One way we can do that is with powerful technologies such as machine learning. Machine learning is a powerful tool which can be very useful when analyzing environmental data, including water quality, and can form a backbone for competent AI systems which help manage and monitor water. When done correctly, it can even predict the quality of a water system going forward in time. Such a versatile method is a huge asset when analyzing data on the quality of water.

    To explore machine learning in water a little bit, we are going to use some groundwater data collected from Locus EIM, which can be loaded into Locus Platform with our API. Using this data, which includes various measurements on water quality, such as turbidity, we will build a model to estimate the pH of the water source from various other parameters, to an error of about 1 pH point. For the purpose of this post, we will be building the model in Python, utilizing a Jupyter Notebook environment.

    When building a machine learning model, the first thing you need to do is get to know your data a bit. In this case, our EIM water data has 16,114 separate measurements. Plus, each of these measurements has a lot of info, including the Site ID, Location ID, the Field Parameter measured, the Measurement Date and Time, the Field Measurement itself, the Measurement Units, Field Sample ID and Comments, and the Latitude and Longitude. So, we need to do some janitorial work on our data. We can get rid of some columns we don’t need and separate the field measurements based on which specific parameter they measure and the time they were taken. Now, we have a datasheet with the columns Location ID, Year, Measurement Date, Measurement Time, Casing Volume, Dissolved Oxygen, Flow, Oxidation-Reduction Potential, pH, Specific Conductance, Temperature, and Turbidity, where the last eight are the parameters which had been measured. A small section of it is below.

    Locus Machine Learning - Data

    Alright, now our data is better organized, and we can move over to Jupyter Notebook. But we still need to do a bit more maintenance. By looking at the specifics of our data set, we can see one major problem immediately. As shown in the picture below, the Casing Volume parameter has only 6 values. Since so much is missing, this parameter is useless for prediction, and we’ll remove it from the set.

    Locus Machine Learning - Data

    We can check the set and see that some of our measurements have missing data. In fact, 261 of them have no data for pH. To train a model, we need data which has a result for our target, so these rows must be thrown out. Then, our dataset will have a value for pH in every row, but might still have missing values in the other columns. We can deal with these missing values in a number of ways, and it might be worth it to drop columns which are missing too much, like we did with Casing Volume. Luckily, none of our other parameters are, so for this example I filled in empty spaces in the other columns with the average of the other measurements. However, if you do this, it is necessary that you eliminate any major outliers which might skew this average.

    Once your data is usable, then it is time to start building a model! You can start off by creating some helpful graphs, such as a correlation matrix, which can show the relationships between parameters.

    Locus Machine Learning - Corr

    For this example, we will build our model with the library Keras. Once the features and targets have been chosen, we can construct a model with code such as this:

    Locus Machine Learning - Construct

    This code will create a sequential deep learning model with 4 layers. The first three all have 64 nodes, and of them, the initial two use a rectified linear unit activation function, while the third uses a sigmoid activation function. The fourth layer has a single node and serves as the output.

    Our model must be trained on the data, which is usually split into training and test sets. In this case, we will put 80% of the data into the training set and 20% into the test set. From the training set, 20% will be used as a validation subset. Then, our model examines the datapoints and the corresponding pH values and develops a solution with a fit. With Keras, you can save a history of the reduction in error throughout the fit for plotting, which can be useful when analyzing results. We can see that for our model, the training error gradually decreases as it learns a relationship between the parameters.

    Locus Machine Learning - Construct

    The end result is a trained model which has been tested on the test set and resulted in a certain error. When we ran the code, the test set error value was 1.11. As we are predicting pH, a full point of error could be fairly large, but the precision required of any model will depend on the situation. This error could be improved through modifying the model itself, for example by adjusting the learning rate or restructuring layers.

    Locus Machine Learning - Error

    You can also graph the true target values with the model’s predictions, which can help when analyzing where the model can be improved. In our case, pH values in the middle of the range seem fairly accurate, but towards the higher values they become more unreliable.

    Locus Machine Learning - Predict

    So what do we do now that we have this model? In a sense, what is the point of machine learning? Well, one of the major strengths of this technology is the predictive capabilities it has. Say that we later acquire some data on a water source without information on the pH value. As long as the rest of the data is intact, we can predict what that value should be. Machine learning can also be incorporated into examination of things such as time series, to forecast a trend of predictions. Overall, machine learning is a very important part of data analytics and the development of powerful AI systems, and its importance will only increase in the future.

    What’s next?

    As the technology around machine learning and artificial intelligence evolves, Locus will be working to integrate these tools into our EHS software. More accurate predictions will lead to more insightful data, empowering our customers to make better business decisions.

    Contact us today to learn how machine learning and AI can help your EHS program thrive

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      AI and Big Data to Drive EHS Decisions via Multi-tenant SaaS

      With data and information streaming from devices like fire hydrants, there is little benefit from raw data, unless a company owning the data has a way to integrate the data into its record system and pair it with regulatory databases and GIS. That is where the advancement in SaaS tools and data sources mashups has helped set the stage for AI as a growing need.

      Humans are not very good at analyzing large datasets. This is particularly true with data at the planetary level that are now growing exponentially to understand causes and fight climate change. Faced with a proliferation of new regulations and pressure to make their companies “sustainable” EHS departments keep adding more and more compliance officers, managers, and outside consultants, instead of investing in technology that can help them. Soon, they will be turning to AI technology to stay on top of the ever-changing regulatory landscape. 

      Locus - Big Data - IoT - AI

      AI, in addition to being faster and more accurate, should make compliance easier. Companies spend too much time and effort on the comprehensive quarterly or annual reporting—only to have to duplicate the work for the next reporting period. The integrated approach, aided by AI, will automate these repetitive tasks and make it easier than just having separate analyses performed on every silo of information before having a conversation with regulators.

      In summary, whether it is being used to help with GHG emissions monitoring and reporting, water quality management, waste management, incident management, or other general compliance functions, AI can improve efficiency, weed out false-positive results, cut costs and make better use of managers’ time and company resources.

      Complex data - Data redundancy

      Another advantage of AI, assuming it is deployed properly, concerns its inherent neutrality on data evaluation and decision making. Time and time again we read in the papers about psychological studies and surveys that show people on opposite sides of a question or topic cannot even agree on the “facts.” It should not be surprising then to find that EHS managers and engineers are often limited by their biases. As noted in the recent best-seller book by Nobel Memorial Prize in Economics laureate Daniel Kahneman, “Thinking, Fast and Slow,” when making decisions, they frequently see what they want, ignore probabilities, and minimize risks that uproot their hopes. Even worse, they are often confident even when they are wrong. Algorithms with AI built-in are more likely to detect our errors than we are. AI-driven intelligent databases are now becoming powerful enough to help us reduce human biases from our decision-making. For that reason, large datasets, applied analytics, and advanced charting and data visualization tools, will soon be driving daily EHS decisions.

      In the past, companies almost exclusively relied upon on-premise software (or single-tenant cloud software, which is not much different from on-premise). Barriers were strewn everywhere. Legacy systems did not talk to one another, as few of the systems interfaced with one another. Getting data into third-party apps usually required the information to be first exported in a prescribed format, then imported to a third-party app for further processing and analysis. Sometimes data was duplicated across multiple systems and apps to avoid the headache of moving data from one to another.  As the world moves to the multi-tenant SaaS cloud, all this is now changing. Customers are now being given the opportunity to analyze not just their company’s data, but data from other companies and different but potentially related and coupled categories via mashups. As customers are doing so, interesting patterns are beginning to emerge.

      The explosion of content—especially unstructured content—is an opportunity and an obstacle for every business today.

      The emergence of artificial intelligence is a game-changer for enterprise EHS and content management because it can deliver business insights at scale and make EHS compliance more productive. There are numerous advantages when you combine the leading multi-tenant EHS software with AI:

      • Ability to handle the explosion of unstructured content where legacy on-premise EHS solutions can’t.
      • AI can organize, illuminate, and extract valuable business insights if all your content is managed in one secure location in the cloud.
      • Locus helps you take advantage of best-of-breed AI technologies from industry leaders and apply them to all your content.

      We are seeing in the most recent NAEM white paper, Why Companies Replace Their EHS&S Software Systems, that people want the ability to integrate with other systems as a top priority.  Once the ability to share/consolidate data is available, AI is not far behind in the next generation of EHS/Water Quality software.

      This concludes the four-part blog series on Big Data, IoT, AI, and multi-tenancy. We look forward to feedback on our ideas and are interested in hearing where others see the future of AI in EHS software – contact us for more discussion or ideas! Read the full Series: Part One, Part Two, Part Three.

      Contact us to learn more about Locus uses IoT and AI

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        SAN FRANCISCO, Calif., 23 July 2019 — Locus Technologies (Locus), the market leader in multi-tenant SaaS water quality, environmental compliance, and sustainability management, today announced that it is going all-in on Amazon Web Services, Inc. (AWS), moving its entire infrastructure to the world’s leading cloud. By moving its flagship product EIM (Environmental Information Management) to AWS this month, Locus will complete its transition to AWS. Locus previously moved its Locus Platform (LP) to AWS in 2018.

        EIM is the world’s leading water quality management software used by many Fortune 500 companies, water utilities, and the US Government since 1999. Among its many features, EIM delivers real-time tools to ensure that water utilities deliver clean water to consumers’ taps and don’t discharge contaminated wastewater above allowable limits to groundwater or surface water bodies like streams, lakes, or oceans.

        EIM generates big data, and with over 500 million analytical records at over 1.3 million locations worldwide, it is one of the largest centralized, multi-tenant water quality management SaaS systems in the world. With anticipated growth in double digits stemming from the addition of streaming data from sensors and many IoT monitoring devices, Locus needed to have a highly scalable architecture for its software hosting. The unmatched performance and scalability of AWS’s offerings are just the right match for powering Locus’ SaaS.

        Because of the scope of its applications, Locus is expecting to leverage the breadth and depth of AWS’s services (including its database systems, serverless architecture, IoT streaming, blockchain, machine learning, and analytics) to automate and enhance the on-demand EHS compliance, sustainability, facility, water, energy, and GHG management tools that Locus’ software provides to its customers.

        Running on AWS’s fault-tolerant and highly performant infrastructure will help support Locus’s everyday business, and will scale easily for peak periods, where reporting demand such as GHG calculation engine or significant emissions incidents like spills can skyrocket scalability demand.

        By leveraging Amazon CloudFront, Amazon Virtual Private Cloud (Amazon VPC), Amazon Elastic Kubernetes Service (Amazon EKS) and AWS Lambda, Locus is migrating to a microservices architecture to create more than 150 microservices that independently scale workloads while reducing complexity in the cloud, thereby enhancing every element of the customer cloud experience. Locus built a data lake on Amazon Simple Storage Service (Amazon S3) and will leverage Amazon Redshift to analyze the vast amount of data it is storing in the cloud, delivering insights and predictive analytics that uncover chemicals trending patterns and predict future emissions releases at various locations.

        Locus intends to leverage AWS IoT services and Amazon Managed Blockchain by building a new native integration to help businesses generate value from the millions of events generated by connected devices such as real-time environmental monitoring sensors and environmental treatment systems controls. AWS IoT is a set of cloud services that let connected devices easily and securely interact with cloud applications like EIM and Locus Platform and other devices. Locus IoT Cloud on AWS allows customers to experience real-time emissions monitoring and management across all their connected sensors and devices. And for customers who want to allow multiple parties to transact (e.g. GHG trading) without a trusted central authority, AWS provides a fully managed, scalable blockchain service. Amazon Managed Blockchain is a fully managed service makes it easy to setup, deploy, and manage scalable blockchain networks that Locus intends to use for emissions management and trading.

        For example, a water utility company that maintains thousands of IoT-enabled sensors for water flow, pressure, pH, or other water quality measuring devices across their dispersed facilities and pipeline networks will be able to use Locus IoT on AWS to ingest and manage the data generated by those sensors and devices, and interpret it in real time. By combining water sensor data with regulatory databases, water utility companies will be able to automatically create an emergency shutdown if chemical or other exceedances or device faults are detected and as such, will be better prepared to serve their customers and environment.

        By combining the powerful, actionable intelligence in EIM and rapid responsiveness through Locus Platform with the scalability and fast-query performance of AWS, customers will be able to analyze large datasets seamlessly on arrival in real time. This will allow Locus’ customers to explore information quickly, find insights, and take actions from a greater variety and volume of data—all without investing the significant time and resources required to administer a self-managed on-premises data warehouse.

        “After 22 years in business, and after evaluating AWS for a year with our Locus Platform, we decided to switch and continue all our business on AWS. We are taking advantage of their extensive computing power, depth and breadth of services and expertise to develop an effective cloud infrastructure to support our growing business and goal of saving the planet Earth by providing and managing factual information on emissions management, all the while reducing operational costs of Locus’ customers,” said Neno Duplan CEO of Locus. “By operating on AWS, we can scale and innovate quickly to provide new features and improvements to our services – such as blockchain-based emissions management – and deliver exceptional scalability for our enterprise customers. With AWS, we don’t have to focus on the undifferentiated heavy lifting of managing our infrastructure, and can concentrate instead on developing and improving apps and services.”

        “By organizing and analyzing environmental, sustainability, and water quality information in the cloud, Locus is helping organizations to understand the impact of climate change on drinking water,” said Mike Clayville, Vice President, Worldwide Commercial Sales at AWS. “AWS’s unmatched portfolio of cloud services, proven operational expertise, and unmatched reliability will help Locus to further automate environmental compliance for companies ranging from local water utilities to multinational manufacturing corporations, to federal government research agencies. ”By choosing to go all-in on AWS, Locus is able to innovate and expand globally, developing new solutions that will leverage comprehensive analytics and machine learning services to gain deeper insights and forecast sustainability metrics that will help deliver clean drinking water to consumers around the world.”

        Read on GlobeNewswire

        More recently, big data has become more closely tied to IoT-generated streaming datasets such as Continued Air Emission Measurements (CEMS), real-time remote control and monitoring of treatment systems, water quality monitoring instrumentation, wireless sensors, and other types of wearable mobile devices. Add digitized historical records to this data streaming, and you end up with a deluge of data. (To learn more about big data and IoT trends in the EHS industry, please read this article: Keeping the Pulse on the Planet using Big Data.) 

        In the 1989 Hazardous Data Explosionarticle that I mentioned earlier, we first identified the limitation of relational database technology in interpreting data and the importance that IoT (automation as it was called at the time) and AI were going to play in the EHS industry. We wrote: 

        “It seems unavoidable that new or improved automated data processing techniques will be needed as the hazardous waste industry evolves. Automation (read IoT) can provide tools that help shorten the time it takes to obtain specific test results, extract the most significant findings, produce reports and display information graphically,” 

        IoT - Internet of Things

        We also claimed that “expert systems” (a piece of software programmed using artificial intelligence (AI) techniques. Such systems use databases of expert knowledge to offer advice or make decisions.) and AI could be possible solutions—technologies that have been a long time coming but still have a promising future in the context of big data. 

        “Currently used in other technical fields, expert systems employ methods of artificial intelligence for interpreting and processing large bodies of information.” 

        Although “expert systems” as a backbone for AI did not materialize as it was originally envisioned by researches, it was a necessary step that was needed to use big data to fulfil the purpose of an “expert”. 

        AI can be harnessed in a wide range of EHS compliance activities and situations to contribute to managing environmental impacts and climate change. Some examples of application include AI-infused permit management, AI-based permit interpretation and response to regulatory agencies, precision sampling, predicting natural attenuation of chemicals in water, managing sustainable supply chains, automating environmental monitoring and enforcement, and enhanced sampling and analysis based on real-time weather forecasts. 

        Parts one, three, and four of this blog series complete the overview of Big Data, IoT, AI and multi-tenancy. We look forward to feedback on our ideas and are interested in hearing where others see the future of AI in EHS software – contact us for more discussion or ideas!

        Contact us to learn more about Locus uses IoT and AI

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          On 12 April 2019, Locus’ Founder and CEO, Neno Duplan, received the prestigious Carnegie Mellon 2019 CEE (Civil and Environmental Engineering) Distinguished Alumni Award for outstanding accomplishments at Locus Technologies. In light of this recognition, Locus decided to dig into our blog vault, share a series of visionary blogs crafted by our Founder in 2016. These ideas are as timely and relevant today as they were three years ago, and hearken to his formative years at Carnegie Mellon, which formed the foundation for the current success of Locus Technologies as top innovator in the water and EHS compliance space.

          Artificial Intelligence (AI) for Better EHS Compliance (original blog from 2016)

          It is funny how a single acronym can take you back in time. A few weeks ago when I watched 60 Minutes’ segment on AI (Artificial Intelligence) research conducted at Carnegie Mellon University, I was taken back to the time when I was a graduate student at CMU and a member of the AI research team for geotechnical engineering. Readers who missed this program on October 9, 2016, can access it online.

          Fast forward thirty plus years and AI is finally ready for prime time television and a prominent place among the disruptive technologies that have so shaken our businesses and society. This 60 Minutes story prompted me to review the progress that has occurred in the field of AI technology, why it took so long to come to fruition, and the likely impact it will have in my field of environmental and sustainability management. I discuss these topics below. I also describe the steps that we at Locus have taken to put our customers in the position to capitalize on this exciting (but not that new) technology.

          What I could not have predicted when I was at Carnegie Mellon is that AI was going to take a long time to mature–almost the full span of one’s professional career. The reasons for this are multiple, the main one being that several other technologies were absent or needed to mature before the promises of AI could be realized. These are now in place. Before I dive into AI and its potential impact on the EHS space, let me touch on these “other” major (disruptive) technologies without which AI would not be possible today: SaaS, Big Data, and IoT (Internet of Things).

          Locus Artificial Intelligence

          As standalone technologies, each of these has brought about profound changes in both the corporate and consumer worlds. However, these impacts are small when compared to the impact all three of these will have when combined and interwoven with AI in the years to come. We are only in the earliest stages of the AI computing revolution that has been so long in the coming.

          I have written extensively about SaaS, Big Data, and IoT over the last several decades. All these technologies have been an integral part of Locus’ SaaS offering for many years now, and they have proven their usefulness by rewarding Locus with contracts from major Fortune 500 companies and the US government. Let me quickly review these before I dive into AI (as AI without them is not a commercially viable technology).


          Big Data

          Massive quantities of new information from monitoring devices, sensors, treatment systems controls and monitoring, and customer legacy databases are now pouring into companies EHS departments with few tools to analyze them on arrival. Some of the data is old information that is newly digitized, such as analytical chemistry records, but other information like streaming of monitoring wireless and wired sensor data is entirely new. At this point, most of these data streams are highly balkanized as most companies lack a single system of record to accommodate them. However, that is all about to change.

          As a graduate student at Carnegie Mellon in the early eighties, I was involved with the exciting R&D project of architecting and building the first AI-based Expert System for subsurface site characterization, not an easy task even by today’s standards and technology. AI technology at the time was in its infancy, but we were able to build a prototype system for geotechnical site characterization, to provide advice on data interpretation and on inferring depositional geometry and engineering properties of subsurface geology with a limited amount of data points. The other components of the research included a relational database to store the site data, graphics to produce “alternative stratigraphic images” and network workstations to carry out the numerical and algorithmic processing. All of this transpired before the onset of the internet revolution and before any acronyms like SaaS, AI, or IoT had entered our vocabulary. This early research led to the development of a set of commercial tools and technological improvements and ultimately to the formation of Locus Technologies in 1997.

          Part of this early research included management of big data, which is necessary for any AI undertaking. As a continuation of this work at Carnegie Mellon, Dr. Greg Buckle and I published an article in 1989 about the challenges of managing massive amounts of data generated from testing and long-term monitoring of environmental projects. This was at a time when spreadsheets and paper documents were king, and relational databases were little used for storing environmental data.

          The article, “Hazardous Data Explosion,“ published in the December 1989 issue of the ASCE Civil Engineering Magazine, was among the first of its kind to discuss the upcoming Big Data boom within the environmental space and placed us securely at the forefront of the big data craze. This article was followed by a sequel article in the same magazine in 1992, titled “Taming Environmental Data,“ that described the first prototype solution to managing environmental data using relational database technology. In the intervening years, this prototype eventually became the basis of the industry’s first multi-tenant SaaS system for environmental information management.

          Locus - Big Data - IoT - AI

          Today, the term big data has become a staple across various industries to describe the enormity and complexity of datasets that need to be captured, stored, analyzed, visualized, and reported. Although the concept may have gained public popularity relatively recently, big data has been a formidable fixture in the EHS industry for decades. Initially, big data in EHS space was almost entirely associated with the results of analytical, geotechnical, and field testing of water, groundwater, soil, and air samples in the field and laboratory. Locus’ launch of its Internet-based Environmental Information Management (EIM) system in 1999 was intended to provide companies not only with a repository to store such data, but also with the means to upload such data into the cloud and the tools to analyze, organize, and report on these data.

          In the future, companies that wish to remain competitive will have no choice but bring together their streams of (seemingly) unrelated and often siloed big data into systems such as EIM that allow them to evaluate and assess their environmental data with advanced analytics capabilities. Big data coupled with intelligent databases can offer real-time feedback for EHS compliance managers who can better track and offset company risks. Without the big data revolution, there would be no coming AI revolution.


          AI and Water Management – Looking Ahead

          There has been much talk about how artificial intelligence (AI) will affect various aspects of our lives, but little has been said to date about how the technology can help to make water quality management better. The recent growth in AI spells a big opportunity for water quality management. There is enormous potential for AI to be an essential tool for water management and decoupling water and climate change issues.

          Two disruptive megatrends of digital transformation and decarbonization of economy could come together in the future. AI could make a significant dent in global greenhouse gas (GHG) emissions by merely providing better tools to manage water. The vast majority of energy consumption is wasted on water treatment and movement. AI can help optimize both.

          AI is a collective term for technologies that can sense their environment, think, learn, and take action in response to what they’re detecting and their objectives. Applications can range from automation of routine tasks like sampling and analyses of water samples to augmenting human decision-making and beyond to automation of water treatment systems and discovery – vast amounts of data to spot, and act on patterns, which are beyond our current capabilities.

          Applying AI in water resource prediction, management and monitoring can help to ameliorate the global water crisis by reducing or eliminating waste, as well as lowering costs and lessening environmental impacts.

          Parts two, three, and four of this blog series complete the overview of Big Data, Iot, AI and multi-tenancy. We look forward to feedback on our ideas and are interested in hearing where others see the future of AI in EHS software – contact us for more discussion or ideas!

          Contact us to learn more about Locus uses IoT and AI

            Name

            Company Email

            Phone

            Tell us about your company's needs

            Locus is committed to preserving your privacy.