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Leveraging Big Data to Improve Traffic Incident Management (2019)

Chapter: Appendix B - Incident Response and Clearance Ontology (IRCO)

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Suggested Citation:"Appendix B - Incident Response and Clearance Ontology (IRCO)." National Academies of Sciences, Engineering, and Medicine. 2019. Leveraging Big Data to Improve Traffic Incident Management. Washington, DC: The National Academies Press. doi: 10.17226/25604.
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Suggested Citation:"Appendix B - Incident Response and Clearance Ontology (IRCO)." National Academies of Sciences, Engineering, and Medicine. 2019. Leveraging Big Data to Improve Traffic Incident Management. Washington, DC: The National Academies Press. doi: 10.17226/25604.
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Suggested Citation:"Appendix B - Incident Response and Clearance Ontology (IRCO)." National Academies of Sciences, Engineering, and Medicine. 2019. Leveraging Big Data to Improve Traffic Incident Management. Washington, DC: The National Academies Press. doi: 10.17226/25604.
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Suggested Citation:"Appendix B - Incident Response and Clearance Ontology (IRCO)." National Academies of Sciences, Engineering, and Medicine. 2019. Leveraging Big Data to Improve Traffic Incident Management. Washington, DC: The National Academies Press. doi: 10.17226/25604.
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Suggested Citation:"Appendix B - Incident Response and Clearance Ontology (IRCO)." National Academies of Sciences, Engineering, and Medicine. 2019. Leveraging Big Data to Improve Traffic Incident Management. Washington, DC: The National Academies Press. doi: 10.17226/25604.
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Suggested Citation:"Appendix B - Incident Response and Clearance Ontology (IRCO)." National Academies of Sciences, Engineering, and Medicine. 2019. Leveraging Big Data to Improve Traffic Incident Management. Washington, DC: The National Academies Press. doi: 10.17226/25604.
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Suggested Citation:"Appendix B - Incident Response and Clearance Ontology (IRCO)." National Academies of Sciences, Engineering, and Medicine. 2019. Leveraging Big Data to Improve Traffic Incident Management. Washington, DC: The National Academies Press. doi: 10.17226/25604.
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Suggested Citation:"Appendix B - Incident Response and Clearance Ontology (IRCO)." National Academies of Sciences, Engineering, and Medicine. 2019. Leveraging Big Data to Improve Traffic Incident Management. Washington, DC: The National Academies Press. doi: 10.17226/25604.
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Suggested Citation:"Appendix B - Incident Response and Clearance Ontology (IRCO)." National Academies of Sciences, Engineering, and Medicine. 2019. Leveraging Big Data to Improve Traffic Incident Management. Washington, DC: The National Academies Press. doi: 10.17226/25604.
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Suggested Citation:"Appendix B - Incident Response and Clearance Ontology (IRCO)." National Academies of Sciences, Engineering, and Medicine. 2019. Leveraging Big Data to Improve Traffic Incident Management. Washington, DC: The National Academies Press. doi: 10.17226/25604.
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Suggested Citation:"Appendix B - Incident Response and Clearance Ontology (IRCO)." National Academies of Sciences, Engineering, and Medicine. 2019. Leveraging Big Data to Improve Traffic Incident Management. Washington, DC: The National Academies Press. doi: 10.17226/25604.
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Suggested Citation:"Appendix B - Incident Response and Clearance Ontology (IRCO)." National Academies of Sciences, Engineering, and Medicine. 2019. Leveraging Big Data to Improve Traffic Incident Management. Washington, DC: The National Academies Press. doi: 10.17226/25604.
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Suggested Citation:"Appendix B - Incident Response and Clearance Ontology (IRCO)." National Academies of Sciences, Engineering, and Medicine. 2019. Leveraging Big Data to Improve Traffic Incident Management. Washington, DC: The National Academies Press. doi: 10.17226/25604.
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Suggested Citation:"Appendix B - Incident Response and Clearance Ontology (IRCO)." National Academies of Sciences, Engineering, and Medicine. 2019. Leveraging Big Data to Improve Traffic Incident Management. Washington, DC: The National Academies Press. doi: 10.17226/25604.
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Suggested Citation:"Appendix B - Incident Response and Clearance Ontology (IRCO)." National Academies of Sciences, Engineering, and Medicine. 2019. Leveraging Big Data to Improve Traffic Incident Management. Washington, DC: The National Academies Press. doi: 10.17226/25604.
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Suggested Citation:"Appendix B - Incident Response and Clearance Ontology (IRCO)." National Academies of Sciences, Engineering, and Medicine. 2019. Leveraging Big Data to Improve Traffic Incident Management. Washington, DC: The National Academies Press. doi: 10.17226/25604.
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181 A P P E N D I X B Incident Response and Clearance Ontology (IRCO) B.1 What Is an Ontology? Big Data in a vacuum is worthless. Big Data only has value when it is leveraged to drive decisions. Although it may be possible to use implicit or existing relationships within data elements to perform simple Big Data analyses, more complex and insightful Big Data analyses will require a more abstract and concise way to express the knowledge that the data represents—a vision or a structure characterizing what the data represents needs to be established. In computer science, this structure is known as an ontology. An ontology is designed to establish a commonly shared vision of a domain. An ontology is a formal naming and definition of the types, properties, and inter-relationships of the entities that really or fundamentally exist in a domain. It is a grammar that, when applied to raw data, gives it an explicit meaning. It is a metaphoric pair of polarized glasses, allowing people to clarify raw data and reveal the information it contains universally. Before attempting to integrate any Big Data datasets and derive insight from them, it is essential to define what these data mean and the relationships that describe the specific context. In other words, it is essential to develop an ontology. This appendix describes the approach and steps taken to develop an incident response and clearance ontology (IRCO). B.2 Development of the IRCO Ontology development can be a challenging endeavor. There is no correct, prescribed development method; one or more viable alternatives always exist, and the best solution often depends on the application of that ontology. In addition, the process is discovery-based, iterative, and likely ongoing. For this reason, ontologies often are qualified as “incomplete” or “reductive” compared to the domain that they attempt to describe. A simple ontology that is reductive and does not cover every single observed case can still be used to map real data and display how data elements relate to each other. It also can reveal ways in which insight might be extracted or inferred from that data. The development of the IRCO included the following steps: • Determine the domain and scope of the ontology. • Re-use existing ontologies to the extent possible. • Enumerate important terms in the ontology. • Define the classes and the class hierarchy. • Define the properties and facets of each class. • Create instances to test the ontology.

182 Leveraging Big Data to Improve Traffic Incident Management workshop focused on the former objective, and the afternoon session of the workshop focused on the latter objective. The workshop was conducted in Phoenix, Arizona, at the Arizona Department of Public Safety (AZDPS). Workshop attendees included members of the Aztec TIM Coalition, as well as subject matter experts from across the country. Specifically, the workshop included representation from the following agencies, organizations, and groups: • Arizona State Troopers. • AZDPS Dispatch Center Manager. • Arizona Department of Transportation (Arizona DOT) Safety. • Arizona DOT Emergency Management. • Arizona DOT Traffic Records. • Arizona DOT Data Systems. • Arizona DOT ALERT. • Maricopa County DOT REACT. • Glendale Fire Department. • Mesa Fire Department. • Maricopa County DOT. • Maricopa Association of Governments (MAG). • Arizona Professional Towing and Recovery Association (APTRA). • Phoenix Metro Towing. • California Department of Transportation (Caltrans). • Minnesota Department of Transportation (Minnesota DOT). • City of Schertz, Texas, Fire Department. • Florida Highway Patrol. • FHWA Arizona Division Office. The incident timeline was used to engage workshop participants in conversation about incident response and clearance. For each phase of the incident timeline, the group reviewed and discussed the following: Who? Does what? When? Where? How? And with what? The next section of this appendix discusses the input from the workshop participants within the context of the ontology development steps. B.2.1 Domain and Scope of the Ontology During the workshop, the concept of an ontology was introduced to participants using a simple ontology called the pizza ontology. The pizza ontology is a well-known ontology in the semantic web community. It was developed for educational purposes by the University of Manchester (University of Manchester 2009). The workshop participants were then walked through the incident timeline and asked various questions to capture the various classes (e.g., vehicle, responder), class relationships, and data entities involved in an incident response. To assist with several of these steps, a workshop was conducted with first responders. The objectives of the workshop were two-fold: (1) gain insights on the vocabulary, entities, and relationships associated with incident response and clearance for the development of the ontology, and (2) identify opportunities to improve TIM through the application of Big Data. The morning session of the all-day

Incident Response and Clearance Ontology (IRCO) 183 using an ontology web development environment in which developers and testers can collaboratively design, test, publish, and maintain the IRCO. Nonetheless, it was established during the workshop that the IRCO should first focus on conceptualizing the response to an incident and how the response relates to the incident itself (i.e., location, time of the day, vehicle, and occupants involved in the incident), as well as the incident environment (i.e., details of the roadway at the incident location, traffic conditions, weather conditions, and social media activities during the response), the personnel, actions, equipment, and response vehicles involved in the response. It also was established that the IRCO should be designed to provide answers to questions such as: • What are the components of an incident response? • Who is involved in an incident response? • Where are the responders during an incident response? • What do responders do during an incident response? • How are traffic and weather conditions related to an incident response? • How does responder training relate to an incident response? • How do social media activities relate to an incident response? B.2.2 Re-Use of External Ontologies in IRCO Rather than design the IRCO from scratch, the IRCO was designed using components from existing ontologies. Information gathered during the workshop was combined with existing traffic incident– related ontologies to establish a basis for the IRCO. Several existing ontologies to describe a traffic incident were available, but most were presented as part of research papers rather than published in an ontology file format such as OWL (Ontology Web Language); therefore, several of the pre-existing ontologies could not be incorporated directly into the IRCO. Of all the ontologies reviewed, a traffic incident ontology developed by Universitat Politècnica de València was chosen as a model for the IRCO. This ontology, the Vehicular Accident Ontology (VEACON), focuses on road safety (Barrachina et al. 2012). Figure B-1 shows a high-level representation of the VEACON ontology and the various data properties of the four main classes (accident, environment, vehicle and occupant). The workshop approach did not produce all the information necessary for developing the ontology. As the workshop progressed through the incident timeline and the definition of the TIM ontology, it became clear that the IRCO designed following the workshop would be rather high-level and simple, and that further development and testing would be needed involving a large group of TIM professionals to develop a consensus on class names, class characteristics, and class relationships, as many options were mentioned by participants without clear, unanimous perspectives. Ideally, such an effort should be done

184 Leveraging Big Data to Improve Traffic Incident Management Source: Barrachina et al. (2012) Figure B-1. The VEACON ontology. Although the VEACON ontology provides a good foundation for the description of an incident, it does not include any information about incident response. Therefore, to capture the distributed and spatiotemporal nature of an incident response, including the various tasks performed by responders using various tools, the LODE (Linking Open Descriptions of Events) ontology was used (Shaw 2010). The LODE ontology allows an event to be described in time, in space, and in terms of who was involved during the event. Figure B-2 shows a graphical representation of the LODE ontology. The LODE ontology also re-uses existing ontologies, such as (1) the DOLCE+DnS Untralite, a light-weight ontology for descriptions and situations (Ontology:DOLCE+DnS Ultralite 2010); (2) the OWL-time ontology, a web ontology language used for temporal concepts (Cox and Little 2017); and (3) the World Wide Web Consortium (W3C) basic geospatial ontology aimed at describing the entities in space (Brickley n.d.).

Incident Response and Clearance Ontology (IRCO) 185 Figure B-2. A visualization of the LODE ontology created using the WebVOWL app. These external ontologies were then imported into the open-source Protégé ontology development tool (Protégé 2016), and classes, object properties, and data properties were added to create the IRCO ontology. The next section lists each of the IRCO ontology components and their definitions. B.2.3 IRCO Classes and Class Hierarchy Table B-1 lists the various classes defined in the IRCO ontology, their super classes, and their definitions.

Table B-1. IRCO ontology classes. Entity Type Superclass(es) Comment Agent Class lode:agent lode:agent Event Class Event E2_Temporal_Entity Event E2_Temporal_Entity Object Class lode:object lode:object Person Class 'Spatial Thing' Agent dcterms:Agent a person 'Spatial Thing' Class Thing geo:wgs84_pos:SpatialThing TemporalEntity Class Thing owl-time:TemporalEntity foaf:media Class Thing foaf:media foaf:organization Class Thing foaf:organization action Class Event action taken during an incident response action_location Class 'Spatial Thing' action location details (latitude, longitude, fips, zipcode, etc.) driver Class occupant a vehicle driver involved in an incident equipment Class incident_object incident response equipment incident Class Event a traffic incident incident_environment Class Thing environment of an incident incident_location Class 'Spatial Thing' incident location details (latitude, longitude, fips, zipcode, etc.) incident_response Class Event a response to a traffic incident occupant Class Person incident_object Person schema:Person an occupant of a vehicle involved in an incident passenger Class occupant a vehicle passenger involved in an incident respondent Class Agent Person Person schema:Person an incident respondent respondent_organization Class foaf:organization an incident respondent organization respondent_vehicle Class equipment a respondent vehicle involved in an incident response roadway Class incident_environment roadway details at the scene of an incident severity Class Thing the severity of an incident

tool Class equipment a tool involved in an incident response traffic_conditions Class incident_environment Event Event Event traffic conditions events around the time and location of an incident vehicle Class incident_object a vehicle involved in an incident weather_conditions Class incident_environment Event Event Event weather conditions events around the time and location of an incident tweet Class social_media Event Event Event a tweet from social media website Twitter around the time and location of an incident incident_time Class TemporalEntity incident time details (start, end, duration, etc.) media Class Thing media such as image, video or sound traffic_conditions_location Class 'Spatial Thing' traffic conditions details (latitude, longitude, fips, zipcode, etc.) traffic_conditions_time Class TemporalEntity traffic conditions events time details (start, end, duration, etc.) response_performance Class Thing the performance of an incident response injury Class Thing details about an individual’s injuries law Class Thing law pertaining to incident responses weather_conditions_time Class TemporalEntity weather conditions time details (start, end, duration, etc.) policies Class Thing policy pertaining to incident responses tweet_location Class 'Spatial Thing' tweet location details (latitude, longitude, fips, zipcode, etc.) weather_conditions_location Class 'Spatial Thing' weather conditions location details (latitude, longitude, fips, zipcode, etc.) social_media Class incident_environment social media events around the time and location of an incident standard_operation_procedure Class Thing standard operation procedure pertaining to incident responses tweet_time Class TemporalEntity tweet time details (start, end, duration, etc.) tweet_image Class foaf:media an image attached to a tweet incident_object Class Object a passive entity involved in an incident action_time Class TemporalEntity action time details (start, end, duration, etc.) respondent_training Class Thing training received by an incident respondent

188 Leveraging Big Data to Improve Traffic Incident Management B.2.4 IRCO Object Properties Table B-2 lists the various object properties defined in the IRCO ontology and their definition. Table B-2. IRCO ontology object properties. Entity Type Comment involved ObjectProperty object or person involved in event illustrate ObjectProperty media illustrate event 'at place' ObjectProperty occurred at place or location 'at time' ObjectProperty occurred at time or during time interval 'involved agent' ObjectProperty involved into event (active participant) owl:topObjectProperty ObjectProperty involved into event (passive participant or object) member ObjectProperty is a member of hasAction ObjectProperty contain an action hasEnvironment ObjectProperty happened during in an environment hasOccupant ObjectProperty vehicle has occupant hasParent ObjectProperty incident has parent incident hasResponse ObjectProperty incident has response hasSeverity ObjectProperty incident has severity hasInjury ObjectProperty person has injury isDerivedFrom ObjectProperty training is derived from hasPerformance ObjectProperty incident response has performance receivedTraining ObjectProperty respondent received a training OccurredAfter ObjectProperty action occurred after another action B.2.5 IRCO Data Properties Table B-3 lists the various data properties defined in the IRCO ontology and their definitions.

Table B-3. IRCO ontology data properties. Entity Type Comment VIN DataProperty vehicle VIN (Vehicle Identification Number) action_type DataProperty the type of an action caused_delay DataProperty delay caused by incident and response Description DataProperty description of an event detection_time DataProperty detection time (TIM performance measure) hazmat_involved DataProperty the presence of hazardous material in an incident heavy_vehicle_involved DataProperty the presence of a heavy vehicle in an incident incident_clearance_time DataProperty incident clearance time (TIM performance measure) incident_identifier DataProperty incident identifier such as a call number incident_response_cost DataProperty the cost of an incident response incident_severity DataProperty the severity of an incident (major, minor, property damage only, etc.) incident_type DataProperty the type of an incident (hazmat, injury, non-injury, fatality, etc.) injury DataProperty the presence of injury in an incident lane_involved_count DataProperty the number of lanes involved in the incident response lane_involved_description DataProperty a description of the lanes involved in the incident response license_plate DataProperty a vehicle license plate make DataProperty the make of a vehicle medical_condition DataProperty the medical condition of a vehicle occupant model DataProperty the model of a vehicle model_year DataProperty the model year of a vehicle number_of_fatality DataProperty the number of fatalities in an incident number_of_injury DataProperty the number of injuries in an incident number_of_minor_injury DataProperty the number of minor injuries in an incident number_of_serious_injury DataProperty the number of serious injuries in an incident number_of_vehicle_involved DataProperty the number of vehicles involved in an incident (continued on next page)

Table B-3. (Continued). Entity Type Comment property_damage DataProperty the presence of property damage in an incident property_damage_cost DataProperty the cost of the property damage of an incident response_time DataProperty response time TIM performance measure roadway_clearance_time DataProperty roadway clearance time (TIM performance measure) the time to return to normal flow time (TIM performance measure) roadway_direction DataProperty the direction of the roadway the incident occurred on roadway_lighting_conditions DataProperty the lighting conditions of the roadway the incident occurred on roadway_name DataProperty the name of the roadway the incident occurred on roadway_surface_condition DataProperty the surface conditions of the roadway the incident occurred on roadway_surface_temperature DataProperty the surface temperature of the roadway the incident occurred on roadway_type DataProperty the type of the roadway the incident occurred on (rural road, highway, etc.) source_name DataProperty the name of the source of the incident and response info time_to_return_to_normal_flow DataProperty the time to return to normal flow time (TIM performance measure) total_lane_at_scene DataProperty the total number of lanes at the scene of the incident verification_time DataProperty Incident verification time (TIM performance measure) weight DataProperty vehicle weight workzone DataProperty the presence of a workzone in an incident occupancy DataProperty traffic occupancy deceased DataProperty if deceased driver_license_number DataProperty driver license number fatality DataProperty the presence of a fatality in an incident speed DataProperty traffic speed volume DataProperty traffic volume

Incident Response and Clearance Ontology (IRCO) 191 B.3 The Incident Response and Clearance Ontology Figure B-3 shows a graphical representation of the resulting IRCO ontology. Online documentation of the IRCO ontology is available at http://timontology.s3-website-us-east-1.amazonaws.com/ and allows readers to navigate through the various classes, object properties, and data properties of the ontology and explore the structure of the IRCO ontology. The IRCO ontology in OWL/RDF-XML format can be downloaded using the following links: • https://s3.amazonaws.com/timontology/owl/irco_ontology.owl. o Going to this website will automatically download and save the file in the downloads folder. • http://visualdataweb.de/webvowl/. The WebVOWL ontology visualization tool can be used to load the IRCO OWL file and render an interactive representation of the IRCO ontology. To access the tool: o Copy the URL into a web browser and go to the website. o Click on “Ontology” at the bottom of the screen. o Click “Select ontology file.” o Open the irco_ontology.owl file saved in the “Downloads” folder. o Click “Upload.” • WebVOWL manual: http://vowl.visualdataweb.org/webvowl.html (Lohmann et al. 2014). Figure B-3. A visualization of the IRCO rendered using WebVOWL.

Abbreviations and acronyms used without definitions in TRB publications: A4A Airlines for America AAAE American Association of Airport Executives AASHO American Association of State Highway Officials AASHTO American Association of State Highway and Transportation Officials ACI–NA Airports Council International–North America ACRP Airport Cooperative Research Program ADA Americans with Disabilities Act APTA American Public Transportation Association ASCE American Society of Civil Engineers ASME American Society of Mechanical Engineers ASTM American Society for Testing and Materials ATA American Trucking Associations CTAA Community Transportation Association of America CTBSSP Commercial Truck and Bus Safety Synthesis Program DHS Department of Homeland Security DOE Department of Energy EPA Environmental Protection Agency FAA Federal Aviation Administration FAST Fixing America’s Surface Transportation Act (2015) FHWA Federal Highway Administration FMCSA Federal Motor Carrier Safety Administration FRA Federal Railroad Administration FTA Federal Transit Administration HMCRP Hazardous Materials Cooperative Research Program IEEE Institute of Electrical and Electronics Engineers ISTEA Intermodal Surface Transportation Efficiency Act of 1991 ITE Institute of Transportation Engineers MAP-21 Moving Ahead for Progress in the 21st Century Act (2012) NASA National Aeronautics and Space Administration NASAO National Association of State Aviation Officials NCFRP National Cooperative Freight Research Program NCHRP National Cooperative Highway Research Program NHTSA National Highway Traffic Safety Administration NTSB National Transportation Safety Board PHMSA Pipeline and Hazardous Materials Safety Administration RITA Research and Innovative Technology Administration SAE Society of Automotive Engineers SAFETEA-LU Safe, Accountable, Flexible, Efficient Transportation Equity Act: A Legacy for Users (2005) TCRP Transit Cooperative Research Program TDC Transit Development Corporation TEA-21 Transportation Equity Act for the 21st Century (1998) TRB Transportation Research Board TSA Transportation Security Administration U.S. DOT United States Department of Transportation

TRA N SPO RTATIO N RESEA RCH BO A RD 500 Fifth Street, N W W ashington, D C 20001 A D D RESS SERV ICE REQ U ESTED N O N -PR O FIT O R G . U .S. PO STA G E PA ID C O LU M B IA , M D PER M IT N O . 88 Leveraging Big D ata to Im prove Traffic Incident M anagem ent N CH RP Research Report 904 TRB ISBN 978-0-309-48071-0 9 7 8 0 3 0 9 4 8 0 7 1 0 9 0 0 0 0

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"Big data" is not new, but applications in the field of transportation are more recent, having occurred within the past few years, and include applications in the areas of planning, parking, trucking, public transportation, operations, ITS, and other more niche areas. A significant gap exists between the current state of the practice in big data analytics (such as image recognition and graph analytics) and the state of DOT applications of data for traffic incident management (TIM) (such as the manual use of Waze data for incident detection).

The term big data represents a fundamental change in what data is collected and how it is collected, analyzed, and used to uncover trends and relationships. The ability to merge multiple, diverse, and comprehensive datasets and then mine the data to uncover or derive useful information on heretofore unknown or unanticipated trends and relationships could provide significant opportunities to advance the state of the practice in TIM policies, strategies, practices, and resource management.

NCHRP (National Cooperative Highway Research Program) Report 904: Leveraging Big Data to Improve Traffic Incident Management illuminates big data concepts, applications, and analyses; describes current and emerging sources of data that could improve TIM; describes potential opportunities for TIM agencies to leverage big data; identifies potential challenges associated with the use of big data; and develops guidelines to help advance the state of the practice for TIM agencies.

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