REACHE - A History Report

A History Report

Prepared by MKTHINK

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REACHE: History Report

TABLE OF CONTENTS 1. REACHE 1

1. Problem

2. Statement of Work

3. Logic 4. Task Overview

5. Cultural Cartography 6. Conclusions

5 6 7 7 8 9 10

REACHE 2

1. Problem

2. Statement of Work

3. Logic 4. Task Overview 5. Conclusions

13 14 15 15 16 21

REACHE 3

1. Problem

2. Statement of Work

3. Logic 4. Task Overview 5. Conclusions

23 24 25 25 26 27

REACHE 4 - ADV. DBMS RD&T PART 1

1. Problem

2. Statement of Work

3. Logic 4. Completed Work 5. Conclusions

29 30 31 31 32 34

REACHE 4 - ADV. DBMS RD&T PART 2

1. Problem

2. Statement of Work

3. Logic 4. Task Overview 5. Conclusions & Next Steps

37 38 38 38 39 42

REACHE 4 - SENSOR KIT

1. Problem

2. Statement of Work

3. Logic 4. Task Overview 5. Conclusions & Next Steps

45 46 46 46 47 50

REACHE: History Report

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REACHE: History Report

1 REACHE 1

Problem

6

Statement of Work

7

Logic

7

Task Overview

8

Cultural Cartography

9

Conclusions

REACHE: History Report

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REACHE 1 REACHE 1 developed the hypothesis that facility and organizational performance could be improved by understanding the interaction between architectural, environmental and cultural factors.

THE PROBLEM The Department of Defense (DOD) has a vested interest in reducing energy consumption without compromising its military base activities. Energy not only contributes to the cost of operations, but it also exposes military bases to increased risks. “One out of every eight casualties in Iraq, between 2003 and 2007, came during fuel deliveries.”

In an effort to reduce the cost and risks associated with energy use, the Office of Naval Research (ONR), an executive branch agency of the DOD, partnered with MKThink to develop site-specific, culturally-sensitive energy efficient solutions. MKThink initiated a research project dubbed REACHE (pronounced “ree-shay”) – Renewable Energy Architecture for Cultural and Human Environments.

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REACHE: History Report

STATEMENT OF WORK REACHE 1 set out to establish a case for evaluating the relationships between renewable energy technology, energy efficiency, environmentally-responsive architecture, and workplace functionality.

The intent of REACHE research for a military application was to align technology to mission needs, reduce critical resource loads, make on-site renewables more feasible, reduce fuel convoys, and thereby combat exposure time. Furthermore, by understanding the relationships between Architecture, Environment, and Culture, this research effort aimed to improve the environmental conditions within forward-operating spaces, reduce the instances of cognitive decline due to air quality or other environmental issues in order to increase battle-readiness and reduce incidents of short-term memory loss or fatigue.

LOGIC The REACHE logic identifies three spheres of overlapping importance when considering energy efficient solutions: Assets (A), Environment (E), and Culture (C).

The REACHE logic conjectures that an integrated understanding of the relationships between Architectural Assets, the Environment, and the Culture of users can improve operational and capital planning strategies that reduce energy consumption and optimize for human performance.

ASSETS

ENVIRONMENT

CULTURE

Assets refer to architecture and technology and includes all man-made structures on a site.

Environment refers to resources and conditions and it includes everything the planet naturally produces – energy, water, and other resources – as well as climate conditions.

Culture refers to individuals and groups, their characteristics and behaviors and how they affect a site.

REACHE: History Report

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TASK OVERVIEW RESEARCH POTENTIAL RELATIONSHIPS BETWEEN THE 3 SPHERES OF REACHE MKThink found several potential relationships between Architecture, Environment and Culture, including: •

Architecture + Environment: buildings impact embodied and operational energy consumption

•

Culture + Environment: collective human behavior impacts the environment and the adoption rate of energy-saving technologies, which in turn affects energy consumption

•

Culture + Architecture: the ideal level of thermal comfort differs depending on culture, necessitating variable architectural cooling strategies.

DEVELOP KEY PERFORMANCE INDICATORS In order to test the hypothesis, MKThink identified the following key indicators of optimal performance: •

Human performance or productivity refers to the efficiency with which a task is carried out

•

Technology delivery is how well technology is aligned with use

•

Capital resource allocation is how business allocate their financial resources and other sources of capital to processes, people, and projects

If any of the performance indicators improve when a strategy integrated the relationships between Architecture, Environment, and Culture is implemented, then the REACHE hypothesis could be proven.

DEMONSTRATE THE VALUE OF THE REACHE LOGIC SET MKThink determined that an integrated understanding for Architecture, Environment, and Culture impacts the identified key indicators of in the following ways: •

Human performance or productivity refers to the efficiency with which a task is carried out

•

Technology delivery is how well technology is aligned with use

•

Capital resource allocation is how business allocate their financial resources and other sources of capital to processes, people, and projects

For instance, by understanding how the materials and layout of a building affect embodied and operational energy consumption, facility managers can make informed design decisions that improve energy efficiency.

If we know how people use space – if they cluster in groups or work individually – and understand their thermal comfort preferences, we can design a space, which maximizes for worker productivity.

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REACHE: History Report

CULTURE CARTOGRAPHY While Architectural Assets and Environmental Resources can be quantified, Cultural factors are more subjective. In an effort to quantify Culture, MKThink developed a Culture Cartography Tool.

The 8-spoked wheel maps an organization’s current culture form against an ideal state – either of the organization in the future or of an aspiring peer organization. Data is collected, mainly from surveys, to populate the cultural categories – diversity, social structure, environmental wellness, human wellness, management, financial, decision MAPPING method, and CULTURE’S technology adeptness.

CULTURE CARTOGRAPHY: FORM WHAT IS IT? Based on the organization’s position on each of the scales they are more broadly categorized as CULTURE CARTOGRAPHY is a patent-pending self-contained, By accurately mapping cultures in terms of eight informed or intuitive and exploratory or restrained.

Once cultural tendencies arecategories, mapped, a quick-to-deploy measurement tool designed to prole cultures at-scale CULTURE CARTOGRAPHY proles give cultural systems analysts a and in time.

It is a data-based technology with particular relevance to robust framework within which to conduct valuable design research that comparative analysis between current and ideal states informs design decisions growing industry demand for reliably and accurately quantifying culture accurately and condently informs our MKThink’s diverse service offerings. for the purposes of informing design, improving services, and optimizing operations.

HOW DOES IT WORK? 1. COLLECT

2. PROCESS

3. MAP

4. ASSESS

5. COMPARE

DATA COLLECTED

MK

DATA INPUT INTO DATA MANAGEMENT SYSTEM

DATA GENERATES CATEGORY SCORES

CULTURE TENDENCY PLOTS INFORM DESIGN STRATEGY

CATEGORY SCORES ARE MAPPED

CULTURE TENDENCY PLOTS COMPARED TO PEERS OR OVER TIME TO QUANTIFY CULTURAL CHANGE

DIVERSITY: a culture’s human heterogeneity and homogeneity in terms of gender, age, skill set, race, etc.

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FINANCIAL: a culture’s nancial management practices with respect to risk, in terms of preference and strategy as well as investment and expenditure

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SOCIAL STRUCTURE: degrees to which a culture inhabits a space in a collective or individualistic way in terms of spatial preferences, proximities, adjacencies, etc.

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DECISION METHOD: the variety of inuences on a culture’s decision-making processes, in terms of data use, the nature and complexity of its think tanks, the ideational interval from problem to solution, etc.

TECHNOLOGY ADEPTNESS: a culture’s technological experience and prociency; their documented and anticipated technology adoption habits and preference

WHY IS THIS VALUABLE? CULTURE CARTOGRAPHY is designed to help organizations answer questions critical to the realization of their potential with solutions rooted in their built environments, outlined in the following example:

PROBLEM

APPROACH

SOLUTION

Liberal Arts College intuits that its graduates are struggling in the new innovation-based economy.

CULTURE CARTOGRAPHY indicates deciencies in Technology Adeptness and collective Social Structure when compared to a high-performing peer.

Strategic design intervention is developed for effective cultural change and monitored with CULTURE CARTOGRAPHY over time.

Who Are You?

REACHE: History Report

Who Do You Want To Be?

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CONCLUSIONS In REACHE 1, MKThink conducted the necessary research to establish an informed hypothesis and developed metrics to test the hypothesis. With the key indicators, performance could be evaluated and the relationships between Architecture, Environment, and Culture could be better understood. The REACHE projects to follow employ the logic developed in REACHE 1 and demonstrate its value.

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REACHE: History Report

REACHE: History Report

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REACHE: History Report

2 REACHE 2

Problem

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Statement of Work

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Logic

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Task Overview

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Conclusions

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REACHE: History Report

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REACHE 2 REACHE 2 created software and hardware products to implement and test the value of the REACHE theory and determine how it can be applied to address common issues of resource and facility use or misuse.

THE PROBLEM REACHE 2 tested the hypothesis established in REACHE 1 that an integrated understanding for the relationships between Architecture, Environment, and Culture can improve operational and capital planning strategies, energy efficiency, and human performance.

REACHE 2 ambitions to develop techniques to measure performance indicators identified in REACHE 1.

Specifically, REACHE 2 responds to the military’s need for a decision-support tool to assist in improved performance for the Navy’s built environment portfolio.

The military practices a decision-making strategy called the OODA Loop, which stands for Observe, Orient/Assess, Decide, and Act, a 4-step process to assist decision-making in extreme environments. The REACHE Decision Support Tools seek to improve the existing process.

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REACHE: History Report

STATEMENT OF WORK In REACHE 2, MKThink further investigated the key indicators of optimal performance, developed an initial sensor kit product to collect data, and developed a Predictive Modeling Tool, which analyzes the data and employs REACHE logic to suggest a decision that optimizes for energy efficiency and facility and human performance.

LOGIC The logic established in REACHE 1 identifies three areas of importance when considering energy efficient solutions: Assets (A), Environment (E) and Culture (C). REACHE 2 expands on this logic by establishing metrics for AEC and by adding three new dimensions - Time (T), Scale (S), and Value (V)- to measure how changes in AEC contribute Value (V) over Time (T) and at Scale (S).

This new logic forms the basis for the AEC – VTS Framework and the 4Daptive Analytical Tool.

REACHE: History Report

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TASK OVERVIEW ESTABLISH METRICS FOR ARCHITECTURAL, ENVIRONMENTAL, AND CULTURAL FACTORS REACHE 2 established metrics for Architecture, Environment, and Culture to quantify their impact on the key performance indicators established in REACHE 1: human performance, technology delivery, capital resource allocation, and energy efficiency.

For instance, Architecture could be quantified as square feet, number of rooms or buildings, depending on scale, thermal mass, fenestration, and classified by its condition and age.

Environment could be quantified by interior air temperature and classified by interior air quality.

Culture could be quantified by the degree of collaboration, the type of organization, and its revenue.

By quantifying AEC, MKThink could measure the impact of each dimension on performance and recommend changes in one or more dimensions to meet performance targets.

DEVELOP THE AEC-VTS FRAMEWORK The AEC-VTS Framework streamlines an otherwise complex and granular data collection and analysis process. The framework recognizes the AEC dimensions, established in REACHE 1, and integrates three new dimensions: Time (T), Scale (S) and Value (V). These are defined as: • Time (T): The intervals, frequencies, and durations of data collections, analyses, and predictive model projections.

•

Scale (S): The level of detail in a study. Scale uses the “architectural ruler” approach to viewing the world where 1:1 scale is close-up, high-detailed and 1:64 scale is broader focus, less-detailed.

•

Value (V): A measurement of the impact that various factors have on performance.

The AEC-VTS Framework is designed to measure the impact of changes in AEC on the performance indicators at scale and over time. The framework conjectures that an understanding of the relationships between AEC over time increases value.

A R C - V T S FRAMEWORK

A - Asset

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C - Culture E - Environment

V - Value

T - Time

S - Scale

Room Type

Occupant Density

Energy Consumption

Safety (Injury Rate)

Operating Hours

Tent

Building Size

Employee Satisfaction

Indoor Air Temperature

Economic

Week

Building

Building Condition

Organization Type

Climate Zone

Resource

Quarter

Site

REACHE: History Report

DEVELOP SENSOR KIT MKThink developed an initial sensor kit product to collect data on indoor environmental conditions. The kits monitored: air temperature, mean radiant temperature, relative humidity, barometric pressure and illuminance (light levels).

To protect the interior componentry, the MKThink product development team housed the kits in portable cases. Each sensor within the kit would be plugged into a data logger tolog data onto the 4Daptive Database over time.

TEST SENSOR KIT MKThink tested the sensor kit product during Balikatan Military Exercises (BK12) at the Crow Valley Air Force base in the Philippines in 2012. MKThink deployed 4 environmental kits into flexible facilities, such as navy tents, and supplemented these with 6 weather stations, 1 infrared people counter, 4 power-monitoring kits and 65 point nodes to log data. In order to validate the sensor data, MKThink researchers collected field measurements with hand-held monitoring devices.

Using sensor data, MKThink analyzed how environmental conditions affected human comfort and energy use and evaluated the effectiveness of the air-conditioning systems in the tents. The Balikatan test revealed performance gaps, or inefficiently run systems caused by the misuse of ventilation and lighting systems. In demonstrating the relationships between AEC and revealing areas where operational strategies and technologies could be implemented to improve facility performance and reduce wasted energy, Balikatan established the value of the AEC framework assessed over time and at different scales.

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DEVELOP 4DAPTIVE TOOL In 2013, MKThink initiated the development of 4Daptive, a patented, cloud-based data management and analytics insights platform. 4Daptive was initially introduced to visualize and analyze the data coming from the sensor kits deployed in the Balikatan field test.

Eventually, MKThink developed the capability for the database to accommodate data from various sources. 4Daptive relates Architectural, Environmental and Cultural data over time and at different scales. The tool allows the user to customize data analysis and reporting by integrating multiple streams of data from various sensor kits. More specifically, the tool has the following analytical capabilities:

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• Correlations

• Regressions

•

LOESS Fit Lines

•

Custom Time/Date Ranges

•

Custom Data Filtering

•

Output Statistics

•

Data Grouping

•

Enhanced Visualization Features

REACHE: History Report

TEST 4DAPTIVE TOOL MKThink tested the 4Daptive tool at the Hawaii Institute of Marine Biology (HIMB) on Coconut Island at Kane’ohe Bay, Oahu, Hawaii in 2013. Deployed environmental sensor kits and infrared people counters collected data in 16 fixed facilities, or non-mobile buildings.

LAND SENSOR LOCATIONS 02/14/13

T ISLAND

MK THIN

INFRARED OCCUPANCY MONITORING (BUILDING LEVEL)

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INFRARED OCCUPANCY MONITORING (ISLAND LEVEL)

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ELECTRIC METER MONITORING (METER LEVEL)

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ELECTRIC METER MONITORING (BUILDING LEVEL)

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TION

After the sensor kits acquired data, researchers uploaded the data to MKThink’s internal servers, Lilapuna Pier (Oahua Side) which cleaned, validated and qualified the data. The 4Daptive tool used this data to analyze 0’ 125’ 250’ patterns and relationships between each AEC factor.

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Sansome Street | San Francisco, CA 94111 | 415.402.0888 mkthink.com

The tool produced visualizations and calculated statistical information such as minimum, maximum and average values for each data point as well as correlation coefficients to better understand how each variable performed independently and the extent to which the variables had an effect on the others. Moreover, certain factors were controlled for so that the effect of all other factors could be accounted for. For instance, using the 4Daptive Tool, the MKThink team received insight that energy usage per person per square foot had reduced during the study period when controlling for square footage and occupancy variations.

REACHE: History Report

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TEST 4DAPTIVE TOOL The test results revealed correlations between environmental conditions and occupancy and between facility type and energy consumption. The results allowed MKThink to expand its database of environmental, cultural and architectural data. The beta field test also revealed 4Daptive’s dynamic data comparison capabilities and informed how the tool could be further refined to provide more complex chart visualizations.

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REACHE: History Report

CONCLUSIONS In REACHE 2, MKThink established the value of the REACHE logic by quantitatively demonstrating the impact of the three spheres – Architecture (A), Environment (E) and Culture (C) – on performance.

MKThink developed an initial environmental sensor kit to collect data, which fed to the 4Daptive Database where the Predictive Modeling Tool analyzed the data and searched for patterns to support decision making. The Predictive Modeling Tool revealed how current environmental conditions and user patterns were affecting facility performance and recommended operational planning strategies accordingly.

The hardware and software tools developed in REACHE 2 supplement the military’s OODA Loop decision cycle by revealing performance gaps and identifying strategies to close them.

REACHE: History Report

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REACHE: History Report

3 REACHE 3

Problem

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Statement of Work

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Logic

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Task Overview

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Conclusions

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REACHE: History Report

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REACHE 3 REACHE 3 refined the sensor kit’s design, functionality and usability and made it more accessible to a broader audience seeking to address issues of facility and/or resource use.

THE PROBLEM After deploying the sensor kit in Balikatan, MKThink recognized that the 4Daptive Database would need an even larger dataset to generate conclusive results. In order to increase data collection, MKThink made the sensor kit easy-to-use and intuitive for untrained personnel, so they could be installed rapidly and at scale.

Success in Balikatan encouraged MKThink to broaden its target market. MKThink realized the potential commercial applications of the REACHE logic set. If the relationships between the Cultural, Architectural, and Environmental factors affecting a business are understood, MKThink conjectured that similar operational and capital performance improvements, energy savings and cultural optimization can be realized.

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REACHE: History Report

STATEMENT OF WORK REACHE 3 focuses on the development of a quick-to-deploy that can be sent to remote areas of extreme environment and collect data related to resource consumption, environmental conditions, technology use, and cultural patterns.

The kit particularly focuses on the Environmental sphere of REACHE to inform energy-efficient solutions in the built environment. REACHE 3 honed the functional and design requirements of the sensor kit, ambitioning to commercialize the product.

LOGIC The REACHE 3 effort is motivated by the idea that energy consumption can be affected by a variety of Architectural, Environmental, and Cultural factors. By employing the REACHE logic, the tools developed in REACHE 3 seek to identify the key drivers of energy use within organizations.

REACHE: History Report

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TASK OVERVIEW IDENTIFY THE KEY DRIVERS OF ENERGY CONSUMPTION Energy use is affected by climate, occupancy, usage modes, and dress, among other factors.

Climatic conditions affect the number of heating and cooling days in a given year. Occupancy affects lighting and plug loads.

The intensity with which occupants use appliances affects energy consumption as well. Perhaps less widely considered: how an occupant dresses affects their thermal comfort and heating and/or cooling preferences.

DEVELOP SENSOR KIT MKThink developed a sensor kit to measure environmental conditions. The team identified the following performance categories for the sensor kit to measure:

• Utilization

•

•

Water quality

•

•

Acoustic comfort

•

Air quality

Thermal comfort Energy consumption

To increase data collection, MKThink decided to make the kits accessible to the nontechnical user.

To ensure this goal was met, MKThink product designers established the following requirements: • Quick and easy to deploy • Able to be operated by non-technical personnel • Able to collect accurate and verified data • Inter-operable • Re-deployable

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REACHE: History Report

TEST SENSOR KIT To evaluate the sensor kit against the requirements, MKThink tested the sensor kit with two high school students, who served as proxies for military personnel.

The test results indicated which aspects of the kits were working as anticipated and which aspects needed further development or refinement.

PACKAGING AND INSTRUCTIONS: Easy, non-technical, and successful.

SENSOR INSTALLATION: Sensors were successfully installed based off of instructions.

USER EXPERIENCE: Kit exceeded our 60% threshold of desired experience criteria.

INTEROPERABILITY: Proves to be one the biggest challenges with off-the-shelf sensors. Our custom designed thermal comfort sensor aims to address this issue.

CONCLUSIONS In REACHE 3, MKThink identified six performance categories for the sensor kit to measure. The data collected augmented MKThink’s understanding for the relationships between Architecture, Environment, and Culture.

The MKThink team developed the kit concept, as a quick and easy to deploy tool that could be operated by a non-technical user, collect accurate and verified data, be inter-operable with a data management system, and re-deployable. To ensure the product designers were meeting their objectives, MKThink tested the prototypes with two high-school aged individuals who were unfamiliar with the kits. The kit exceeded the 60% threshold of desired experience criteria.

However, the software team found it difficult to transfer information from off-the-shelf sensors to the database management system. The test results helped the product development team refine the user experience with the kit.

Incorporating the feedback collected in testing, MKThink developed a final prototype to be sent out to remote areas of extreme environments to collect data related to resource use, environmental conditions, technology use, and cultural patterns.

The REACHE 3 Sensor Kit proved the value in collecting environmental data. Data about the environmental conditions of a space informs the optimal layout and the inclusion and operation of appliances. For instance, environmental data can inform the orientation of passive and resource-efficient cooling strategies. A reduced reliance on resource-intensive systems, such as HVAC, provides significant utility savings. Data about organizational culture can improve workplace conditions that promote employee health and well-being, reduce sick days, and improve retention rates.

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REACHE: History Report

4 REACHE 4 ADV. DBMS RD&T PART 1

Problem

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Statement of Work

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Logic

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Completed Work

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Conclusions

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REACHE: History Report

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REACHE 4: ADV. DBMS RD&T PART 1 REACHE 4: Adv. DBMS RD&T P1 established the capability to capture unstructured cultural and architectural data. The intent of this phase was to augment data acquisition capabilities and thereby, provide more meaningful solutions to questions of space and energy use.

THE PROBLEM Up to this point, REACHE projects succeeded in collecting environmental data. REACHE 4 Adv. DBMS Part 1 populated the remaining two REACHE spheres – Architecture and Culture – with data.

A more robust REACHE dataset leads to more impactful solutions.

REACHE 4: Advanced Database Management System Research, Development & Testing (DBMS Part 1 where expanded types of data that could be collected in by part leveraging language of into RD&T) a context itsthemeasurable quantity indicates the natural performance processing (NLP) and other software techniques and programs to form the Advanced Database es a REACHE Key Indicator .

Management System (Adv. DBMS).

its pa

In REACHE 2 and 3, MKThink identified that architectural and cultural data was mostly inaccessible because existed in unstructured formats, such as in visual, and other changes factors areitcompiled in the REACHE Engine andsymbolic, can aidtextual in determining non-numeric formats. 80-85% of an organization’s data exists in unstructured formats, but in onemany dimension without sacrificing another. organizations do not have access toititin because it is complex to acquire and synthesize.

MKThink determined that unstructured could enhance an analyst’s understanding of the subjective factors contributing to energy use, such as organizational culture. REACHE 4 set out to develop systems to effectively collect and manage unstructured information.

EI X TT ION

PREPARED FOR: Office of Naval Research

In support of the University of Hawaii’s Project “Hawaii Energy andBY: Environmental PREPARED MKThink Technologies Initiative,” the Hawaii Natural Energy Institute (HNEI) partnered with MKThink to improve decisions related to sustainable energy solutions 30

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REACHE: History Report

STATEMENT OF WORK REACHE 4 Adv. DBMS Part 1 addressed the lack of usable cultural and architectural data. MKThink leveraged NLP to develop software that could capture and integrate unstructured video, text, image, audio and handwriting data into 4Daptive.

LOGIC This research effort hinges on the REACHE logic, that a heightened understanding for the Architectural and Cultural assets within an organization can refine planning strategies.

For instance, by capturing the cultural and architectural data, an organization can better understand space and employee usage patterns. Findings might cause the organization to consider vacating and selling an underutilized building and optimizing space elsewhere.

Buildings intentionally designed for how they will be used, waste less energy.

With cultural data, data analysts can also get a better idea of employee satisfaction and their opinions about certain facilities by synthesizing survey results. Findings can inform design decisions, tailored to human wellness.

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COMPLETED WORK CONDUCT MARKET RESEARCH MKThink conducted market research to narrow down the software tools and unstructured data types that would be most beneficial to those involved in making energy decisions, such as Sustainability Coordinators, Energy Engineers and Facilities Managers.

DEVELOP 4DAPTIVE SOFTWARE MKThink developed a cloud-based Advanced Database Management System (Adv. DBMS). At the core of the Adv. DBMS is an analytic engine, which searches for relationships between Architecture, Environment, and Culture, and presents them to the user to help them make decisions.

Functional and non-functional requirements were developed to define the tool’s capabilities.

Functional requirements are elements of the tool that directly affect an external user’s ability to effectively navigate the tool. These include interface design and system navigation.

Non-functional requirements, on the other hand, are the components of the tool that are necessary for internal mechanisms of the tool to follow certain standards and guidelines when processing and transforming raw data. These include database management systems and data cleaning, backup and storage.

More specifically, the functional requirements are: • • • • • • 32

Client Creation Project Creation Manual Data Import Automatic Data Import Unstructured Data Data Grid

• • • • •

Chart Configuration Axis Settings Series Type Data Settings Visual Settings

• Chart Interaction • Chart Saving • Exploratory Research • Statistical View • Description

REACHE: History Report

DEVELOP 4DAPTIVE SOFTWARE CONTINUED And the non-functional requirements are: • Database Management System • Database Management Tools and Data Organization • Data Backup, Recovery and

Performance Monitoring • ETL, CDC and Other Integration Tools • Data Migration and Conversations • Data Purging • Data Visualization and Analysis • Data Control • Data Cleaning Support

• Quality Improvement • User Admin • Unauthorized Access Security • Metadata Content • Metadata Access • Metadata Reporting • Global and Local Reference Data Management • Reference Data Application • Storage and Access

These requirements informed the analytical engine, which leverages existing NLP software, MonkeyLearn, to sift through content in Tweets, webpages and PDF files to extract key words and relevance. MKThink also demonstrated the ability to structure video footage using PrismSkylabs, a Computer Vision (CV) program.

MKThink could analyze utilization rates of spaces by determining occupancy and dwell times through dwell analysis, heat and path mapping in unstructured video analysis. MKThink could also produce reports using the processed structured and unstructured data.

On the 4Daptive platform, structured data, such as interior air quality, could be visualized alongside previously unstructured data, such as Twitter posts. This comparative analysis might inform how an environmental factor, such as interior air quality, affects attitudes and cognitive ability, cultural measures.

TEST 4DAPTIVE SOFTWARE MKThink tested the 4Daptive software prototype to ensure it met functional and non-functional requirements.

Specifically, MKThink was looking to see if previously unstructured data could be processed and visualized in combination with structured data, such as data on environmental conditions. Test results confirmed the tool’s capabilities and provided insight into how the database management system could be further improved. The features of the tool that were identified as in need of further development were the project database set-up, the visualizer and the event detection capability.

Enhancing the tool’s set-up, interface and analytical engine were marked as key next steps.

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CONCLUSIONS In REACHE 4, MKThink established the capability to acquire, process, manage and analyze structured and unstructured data. MKThink developed an analytic engine within the Adv. DBMS to acquire, synthesize and analyze cultural and architectural data.

With these new capabilities, users could understand how Architectural, Environmental, and Cultural factors were impacting environmental conditions, facility operations and human wellness.

As a result, users were able to make decisions to optimize their facilities and increase energy efficiency.

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5 REACHE 4 ADV. DBMS RD&T PART 2

Problem

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Statement of Work

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Logic

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Task Overview

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Conclusion & Next Steps

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REACHE 4: ADV. DBMS RD&T PART 2 REACHE 4: Adv. DBMS RD&T P2 refined the sensor kit and the 4Daptive Analytical tool developed in earlier phases to expand the types of data that could be gathered and analyzed. In leveraging computer vision to capture activity patterns and equipping 4Daptive with cognitive computing capabilities, REACHE 4 refined decisions to space, facility and energy use.

THE PROBLEM REACHE 4: Adv. Advanced Database Management System Research, Development & Testing (DBMS RD&T) Part 1 established the capacity to acquire unstructured test and video data, primarily to capture architectural and cultural data. As a continuation of this work, REACHE 4 Adv. DBMS RD&T Part 2 sought to connect data sets to decisions.

STATEMENT OF WORK REACHE 4 – Adv. DBMS RD&T Part 2 intended to enhance both the REACHE logic and tools by connecting structured and unstructured data sets to culturally-sensitive design solutions for optimal performance and energy efficiency. MKThink refined the Adv. DBMS to perform the following analytic capabilities: ADVANCED ANALYTICS (AA): The application of data to problem-solving COGNITIVE COMPUTING (CC): The use of computer-driven intelligence to suggest decisions and refine analytic algorithms with limited or without the aid of human intelligence

LOGIC REACHE 4 – Adv. DBMS RD&T Part 2 employed the REACHE logic, examining how Culture, Assets and Resources interact for the purpose of optimizing facility performance. The Adv. DBMS receives data about the Cultural, Architectural, and Environmental factors at play in the user’s organization and runs its algorithms to suggest changes in the organization’s culture, built environment, and/or resource consumption patterns to optimize performance.

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TASK OVERVIEW IDENTIFY THE KEY DRIVERS OF ENERGY CONSUMPTION The MKThink research team extensively reviewed literature on the practice of Advanced Analytics (AA) and Cognitive Computing (CC).

Cognitive Computing is a branch of artificial intelligence with various technological capabilities, including machine learning, natural language processing, speech recognition, computer vision, human-computer interaction, and robotics.

After evaluating the various applications of Cognitive Computing, MKThink decided to pursue developing a machine learning capability within the Adv. DBMS. Machine learning is the ability of computer systems to improve their performance by exposure to data without the need to follow explicitly programmed instructions.

Machine learning can be supervised, unsupervised, or reinforced. Supervised learning programs label and train input to known outputs and integrate new data to verify the outputs. Unsupervised learning programs do not label input data and do not have known outputs; the model groups the data by similarities or other data-implicit attributes. Reinforcement learning learns actions to maximize reward in a training environment. Based on MKThink’s space-based problem sets, mostly with labeled data, the team deemed supervised learning most relevant.

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DEVELOP REQUIREMENTS The MKThink team decided that the analytical tool within the Adv. DBMS needed to be: ADAPTIVE: The system must learn as information changes and resolve ambiguity, particularly through the

use of a feedback loop INTERACTIVE: The use of computer-driven intelligence to suggest decisions and refine analytic algorithms

with limited or without the aid of human intelligence ITERATIVE &: The system must aid in defining a problem by asking questions or finding additional source STATEFUL input if a problem statement is ambiguous or incomplete CONTEXTUAL: The system must understand, identify, and extract contextual elements such as meaning,

syntax, time, location, appropriate domain, regulations, user’s profile, process, task, and goal

DEVELOP STRATEGY TOOL MKThink has long used a strategy tool to uncover operational inefficiencies and opportunities for improvement with its clients. In REACHE 4 Adv. DBMS RD&T Part 2, the team decided to automate the strategy tool and incorporate it into the Adv. DBMS user interface as a way to test cognitive computing capabilities.

The strategy tool was originally developed by MKThink to provide a simple graphic image that allows people to perceive relative quantitative differences between of ideal and ‘goal’ states on a 4-axis graph.

Quantities on the 4 axes equal a central unit, represented by the area of the shape that forms on the axes. Most commonly, MKThink has measured for Effective Use Hours (EUH), but strategists have also experimented with central units that measure Annual Kilowatt-Hours (AkWh) and a Cultural Satisfaction Index.

MKThink identified an array of problem typologies, which could be solved with the strategy tool: • Holistic • Resource • Asset • Human Factors Holistic problems solve for revenue in dollars or another metric representing a holistic challenge within the organization. Resource problems solve for AkWh or another metric having to do with resource use. Asset problems are measured with EUH, a measure of how effectively a space is being used. A human factor problem is solved for by measuring user satisfaction.

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The horizontal axes represent a certain asset that is involved in the production of the central unit.

The right axis represents the Quantity of the asset and the axes extending to the left represents the Efficiency with which said asset is being used. In many cases, the horizontal axis has represented space, with gross square feet (GSF) as the metric for Quantity and GSF / full time employee (FTE), a metric of density, on the left to represent how efficiently the space is being used. However, the horizontal axes could also represent the number of full-time employees (FTE) or energy consumption, depending on the problem type.

The vertical axis always represents time. The top axis can represent Operating Time at any scale (hours / day, hours / week, hours / year, etc‌). The bottom axis represents the Quality or effectiveness of said time. The Quality axis tries to quantify how improvements in the built environment (e.g. higher technology) affect performance output or human productivity.

In the automated iteration of the strategy tool, the user logs on to the 4Daptive website interface and is prompted to input a problem statement by filling in a series of blanks, in the form of a Mad Lib sentence. The blanks are prepopulated with drop down menus as well as the option to enter custom values.

Once the system has gathered sufficient information about the user’s problem, it categorizes it as either a holistic, resource, asset, or human factors problem. The system then asks the user to set a target value for the central unit they aspire to achieve. Based on the problem type, the system recommends axes metrics to calculate the central unit. However, the user also has the opportunity to create custom axes metrics to adhere more closely with how they are capable of or prefer to solve the problem.

In this iteration of the strategy tool the user also has the opportunity to set priorities they would like to maximize for in their solution, such as cost, energy, health, and satisfaction. These settings will inform the system to generate an optimized solution based on the user’s priorities.

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The strategy tool generates shape visualizations of the user’s current state, the target shape, which the user set, and recommended shape, based on the user’s priorities.

The user can compare their shape to a benchmark, their peers, or themselves over time and constantly add more data to refine the computer’s modeled predictions.

CONCLUSION + NEXT STEPS In this second phase of REACHE 4, MKThink equipped the Adv. DBMS with analytic capabilities to not only organize data and reveal patterns, but to suggest decisions based on the user’s goals and values.

MKThink is equipping the strategy tool with cognitive computing capabilities, notably a feedback loop, that will learn from patterns in user input and tool output. This will help refine the tool’s algorithms to suggest more accurate and viable solutions. MKThink is continuing to define this feedback loop and its technical implications.

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6 REACHE 4 SENSOR KIT

Problem

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Statement of Work

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Logic

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Task Overview

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Conclusions

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REACHE 4: SENSOR KIT THE PROBLEM The REACHE 3 Sensor Kit effectively evaluated environmental quality, but inadequately collected occupancy data due to limitations in sensor technology. Human factors have been traditionally difficult, if not impossible, to measure and understand, but have significant impact on actual performance.

The REACHE 4 contract recognizes the importance of filling this gap in the data. The REACHE 4 Sensor Kit focuses on the relationship between Culture and Architecture by collecting data on room dimensions, utilization, occupancy, and user activity. Based on the REACHE 3 field testing and post-field testing analysis, MKThink determined that the REACHE 4 Sensor Kit needed to be smaller, more durable, and simpler to deploy

STATEMENT OF WORK The mission of this section of work is to develop Sensor Kits for field deployment and for use in testing the Advanced Database Management System (Adv. DMS).

LOGIC The REACHE 4 Sensor Kit seeks to make the REACHE dataset more robust by adding Architectural and Cultural data.

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TASK OVERVIEW CONDUCT MARKET RESEARCH MKThink researched state-of-the-art sensors and housing units for sensor packages in order to identify gaps in the sensor market. The team reviewed eight different occupancy and utilization sensor technologies across nineteen companies. The findings combined with MKThink’s prior knowledge, acquired during the REACHE 2 and 3 projects as well as its experience using sensors with clientele, informed the design and functional requirements for the REACHE 4 Sensor Kit.

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DEVELOP REQUIREMENTS At the onset of REACHE 4, MKThink established the goal to exceed three previous design requirements established in REACHE 3, to make the sensor kit smaller, more durable, and easier to deploy. Additionally, MKThink committed to two new design requirements, to make data collection private (non-identifying) and affordable. The updated design requirements are outlined below: • Quick & Easy (Easy-to-Use) • Non-technical • Accurate & Verifiable • Inter-operable • Redeployable (Durable) • Private • Affordable The REACHE 3 Sensor Kit effectively evaluated environmental quality, but did not collect Utilization, Occupancy, and Activity (UOA) and Architectural Asset (AA) data. After evaluating the current sensor market, MKThink decided to develop its own UOA and AA Sensor Kit. To fill the identified gap in the UOA and AA sensor market, MKThink established the following functional requirements for the REACHE 4 Sensor Kit: • • • • • • •

Field dimensions: the length, width, and height of features in the camera’s field of vision Utilization: time-stamped data indicted when given rooms are being used Occupancy: time-stamped data indicating the number of people in a given room Activity: user behaviors in space, including: The location of individuals relative to the field The proximity of individuals to one another The path of movement of individuals within space

These data layers reveal opportunities to mitigate operational inefficiencies. The REACHE 4 Baseline Measurement collects quantitative and qualitative data about users in space to inform design solutions better catered to the user experience. The REACHE 4 Kit informs solutions to maximize energy efficiency and user productivity while maintaining the cultural integrity of the organization.

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DEVELOP SENSOR KIT MKThink developed a sensor kit to measure UOA and AA. To meet the design requirements, the team developed a custom housing unit for the sensor kit in-house using a 3D-printer. The housing unit consolidates all sensor kit components for easy deployment and provides durability to protect the kit if it drops.

A camera serves as the sensing device onboard the Sensor Kit. The sensor kit is paired with a Computer Vision software, a form of Cognitive Computing, which employs human-coded data sets and custom algorithms to translate the video feed into reportable data.

With Computer Vision, the REACHE 4 sensor kits are able to collect both Utilization, Occupancy, and Activity (UOA) data and Architectural Asset (AA) data, representing the Cultural and Architectural spheres of REACHE. The Computer Vision algorithms can detect people, track their movement and location throughout space, and divide the field of view into zones.

The algorithm uses an online database of human forms to detect people in the video feed. The algorithm totals the number of detected people to generate an occupancy count. To track people throughout space, the algorithm places a tracking point at the person’s center. If a person’s center is detected with a certain radius frame-to-frame, then the computer identifies them as the same person and tracks their movement throughout the field. The individual’s location within the space and their proximity to other people or areas is also timestamped and recorded. Additionally, users have the option to assign zones around areas of interest and track occupant activity in and out of the zones.

In addition to UOA data, the Computer Vision algorithm can also determine space dimensions.

If the measurements of a set of parallel lines in the camera’s field of view is known and identified by the user, a vanishing point can be calculated, and pixel distances can be transformed to actual distances.

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CONCLUSION + NEXT STEPS The REACHE 4 Sensor Kit is smaller, more durable, and easier to deploy. The Kit responds to gaps in the sensor market and the previous kits developed under REACHE contracts. The Kit can effectively collect UOA and AA data to augment REACHE datasets and allow organizations to make more informed decisions.

The REACHE 4 Sensor Kit needs to be tested to ensure usability and function. Results from these tests will inform necessary steps to bring the Kit to market.

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