{"project":{"acronym":"","projectId":93412,"title":"Collective Inference based Data Analytics System for Post Operations Analysis","primaryTaxonomyNodes":[{"taxonomyNodeId":10536,"taxonomyRootId":8816,"parentNodeId":10533,"level":3,"code":"TX01.1.3","title":"Cryogenic","definition":"Cryogenic propulsion systems or subsystems operate with propellants that are liquefied gases at low temperatures.","exampleTechnologies":"Liquid oxygen (LO2), methane (CH4) pressure-fed main engine LO2, CH4 pump-fed main engine LO2, liquid hydrogen (LH2) reaction and attitude control engine, LOX/RP, LH2/LOX based engine","hasChildren":false,"hasInteriorContent":true}],"startTrl":2,"currentTrl":4,"endTrl":4,"benefits":"The proposed innovation, CIDAS-P, is applicable to NASA research in the areas of Trajectory Based Operations (TBO) and Airspace Technology Demonstrations (ATD). It provides a tool for evaluating performance of airport, TRACON and enroute traffic under the management of new NASA research-products developed by these projects. CIDAS-P can also be used as a 'grading' system for ATD-2 operations, for evaluating and classifying operation types in real-time to inform switching between multiple ATD-2 scheduling strategies (e.g., conservative gate-holding, aggressive gate pushbacks). As applied to NASA's Data Science research group's work on identifying operational anomalies, CIDAS-P can be used to rank or measure safety of operations during the identified anomalies, thus providing a hitherto missing capability to automatically identify scenarios falsely tagged as anomalous by the research group's algorithms.
A direct application for the proposed technology, CIDAS-P, is as a decision support tool (DST) to be used at airports, airlines or FAA facilities for analyzing root causes for observed operational efficiencies or irregularities at the end of a day of operations. ATAC is well-placed to provide an operational POA capability to airports, airlines and FAA facilities by integrating CIDAS-P into one of its commercial tools that have been or are been used by some of these entities for other purposes. Another alternative is for ATAC to license the CIDAS-P software to these entities for direct integration within their own tools. The proposed technology can also be infused into DSTs to support the FAA's Plan Execute Review Train and Improve (PERTI) initiative, whose goal is to translate post-event reviews into operational improvements in a repeatable manner. FAA recently started PERTI to address current impediments to improvements in NAS performance. PERTI defines new operational roles and processes to enable integration of analytics into training, operational planning, post-event analysis and training. The current-day manual POA process is inadequate to fulfill PERTI's ambitious goal of translating post-event reviews into operational improvements in a repeatable manner, and it is recognized that new automated POA tools are required. CIDAS-P, integrated into ATAC's commercial platforms or directly into FAA systems, fulfills this need.","description":"Current-day capabilities for performing post operations analysis (POA) of air traffic operations at airports, airlines and FAA facilities are mostly limited to creating reporting type of analysis results which compare mean values of key performance indicators against the respective expected nominal levels (e.g., average daily delay). This single point comparison method does not directly enable a POA analyst to identify the root-cause for a particular observed inefficiency, nor does it help in identifying a solution for mitigating that inefficiency. This SBIR develops a machine learning based approach for improving POA and for potentially making it more autonomous. We call this tool Collective Inference based Data Analytics System for POA (CIDAS-P). CIDAS-P will provide airport, airline, FAA and NASA personnel with a fast, flexible and streamlined process for analyzing the day-of-operations, rapidly pinpointing exact causes for any observed inefficiencies, as well as recommending actions to be taken to avoid the same inefficiencies in the future. It does this by developing an innovative, collective inference algorithm for cross-comparing performance of the same facility on different days as well as cross-comparing performance across different facilities. The algorithm leverages sophisticated probabilistic modeling techniques that consider the subtle nuances by which cross-facility and cross-day operational scenarios differ to enable apples-to-apples comparisons across traffic scenarios and identify what works well and what does not in similar situations. User acceptance of NASA Trajectory Based Operations research products stands to benefit from CIDAS-P because CIDAS-P's automated recommendations can help identify and fix problems with these products early on in their deployment life-cycle.","startYear":2017,"startMonth":6,"endYear":2017,"endMonth":12,"statusDescription":"Completed","principalInvestigators":[{"contactId":205203,"canUserEdit":false,"firstName":"Jason","lastName":"Bertino","fullName":"Jason L Bertino","fullNameInverted":"Bertino, Jason L","middleInitial":"L","primaryEmail":"jlb@atac.com","publicEmail":true,"nacontact":false}],"programDirectors":[{"contactId":206378,"canUserEdit":false,"firstName":"Jason","lastName":"Kessler","fullName":"Jason L Kessler","fullNameInverted":"Kessler, Jason L","middleInitial":"L","primaryEmail":"jason.l.kessler@nasa.gov","publicEmail":true,"nacontact":false}],"programExecutives":[{"contactId":215154,"canUserEdit":false,"firstName":"Jennifer","lastName":"Gustetic","fullName":"Jennifer L Gustetic","fullNameInverted":"Gustetic, Jennifer L","middleInitial":"L","primaryEmail":"jennifer.l.gustetic@nasa.gov","publicEmail":true,"nacontact":false}],"programManagers":[{"contactId":62051,"canUserEdit":false,"firstName":"Carlos","lastName":"Torrez","fullName":"Carlos Torrez","fullNameInverted":"Torrez, Carlos","primaryEmail":"carlos.torrez@nasa.gov","publicEmail":true,"nacontact":false}],"projectManagers":[{"contactId":74149,"canUserEdit":false,"firstName":"Chok Fung","lastName":"Lai","fullName":"Chok Fung Lai","fullNameInverted":"Lai, Chok Fung","primaryEmail":"chok.f.lai@nasa.gov","publicEmail":true,"nacontact":false},{"contactId":461333,"canUserEdit":false,"firstName":"Theresa","lastName":"Stanley","fullName":"Theresa M Stanley","fullNameInverted":"Stanley, Theresa M","middleInitial":"M","primaryEmail":"theresa.m.stanley@nasa.gov","publicEmail":true,"nacontact":false}],"website":"","libraryItems":[{"file":{"fileExtension":"pdf","fileId":299049,"fileName":"SBIR_2017_1_BC_A3.01-8685","fileSize":38492,"objectId":295584,"objectType":{"lkuCodeId":889,"code":"LIBRARY_ITEMS","description":"Library Items","lkuCodeTypeId":182,"lkuCodeType":{"codeType":"OBJECT_TYPE","description":"Object Type"}},"objectTypeId":889,"fileSizeString":"37.6 KB"},"files":[{"fileExtension":"pdf","fileId":299049,"fileName":"SBIR_2017_1_BC_A3.01-8685","fileSize":38492,"objectId":295584,"objectType":{"lkuCodeId":889,"code":"LIBRARY_ITEMS","description":"Library Items","lkuCodeTypeId":182,"lkuCodeType":{"codeType":"OBJECT_TYPE","description":"Object Type"}},"objectTypeId":889,"fileSizeString":"37.6 KB"}],"id":295584,"title":"Briefing Chart","description":"Collective Inference based Data Analytics System for Post Operations Analysis, Phase I Briefing Chart","libraryItemTypeId":1222,"projectId":93412,"primary":false,"publishedDateString":"","contentType":{"lkuCodeId":1222,"code":"DOCUMENT","description":"Document","lkuCodeTypeId":341,"lkuCodeType":{"codeType":"LIBRARY_ITEM_TYPE","description":"Library Item Type"}}},{"caption":"Collective Inference based Data Analytics System for Post Operations Analysis, Phase I Briefing Chart Image","file":{"fileExtension":"gif","fileId":295258,"fileName":"SBIR_2017_1_BC_A3.01-8685","fileSize":25816,"objectId":291783,"objectType":{"lkuCodeId":889,"code":"LIBRARY_ITEMS","description":"Library Items","lkuCodeTypeId":182,"lkuCodeType":{"codeType":"OBJECT_TYPE","description":"Object Type"}},"objectTypeId":889,"fileSizeString":"25.2 KB"},"files":[{"fileExtension":"gif","fileId":295258,"fileName":"SBIR_2017_1_BC_A3.01-8685","fileSize":25816,"objectId":291783,"objectType":{"lkuCodeId":889,"code":"LIBRARY_ITEMS","description":"Library Items","lkuCodeTypeId":182,"lkuCodeType":{"codeType":"OBJECT_TYPE","description":"Object Type"}},"objectTypeId":889,"fileSizeString":"25.2 KB"}],"id":291783,"title":"Briefing Chart Image","description":"Collective Inference based Data Analytics System for Post Operations Analysis, Phase I Briefing Chart Image","libraryItemTypeId":1095,"projectId":93412,"primary":true,"publishedDateString":"","contentType":{"lkuCodeId":1095,"code":"IMAGE","description":"Image","lkuCodeTypeId":341,"lkuCodeType":{"codeType":"LIBRARY_ITEM_TYPE","description":"Library Item Type"}}}],"transitions":[{"transitionId":69404,"projectId":93412,"partner":"Other","transitionDate":"2018-05-01","path":"Advanced To","relatedProjectId":101847,"relatedProject":{"acronym":"","projectId":101847,"title":"Collective Inference Based Data Analytics System for Post Operations Analysis Phase II","startTrl":4,"currentTrl":6,"endTrl":6,"benefits":"The CIDAS-P technology has application across multiple NASA projects. CIDAS-P's automated, results-oriented post operations analysis (POA) complements ongoing ATD-2 CLT operations analysis efforts. Phase II continuous ops monitoring and NEC surface-airspace ops analysis capabilities support development of ATD-2's Strategic Scheduling component, and accelerates progress towards the next phase of ATD-2. CIDAS-P collective inference analysis on weather-driven en route rerouting scenarios can accelerate evaluation and continuous improvement of ATD-3 rerouting technologies. ATM-X's IDM research will benefit by leveraging CIDAS-P as a reliable, results-oriented method for evaluating the effectiveness of enroute TBFM-TFMS coordination strategies. CIDAS-P's airline ops analysis use case supports NASA Airline Operations Research Group (AORG) in its objective of infusing NASA-funded technologies into airline tools. CIDAS-P can also significantly improve System Wide Safety (SWS) project's anomaly detection algorithms, by providing reliable, automated collective inference based guidance on whether the identified safety alerts are false positives or missed safety alerts. Research into future diverse operations (UAM, IDO) also stands to benefit by CIDAS-P enabled continuous operations improvement guidance. Working software prototypes and collective inference algorithms can be incorporated into NASA software analysis platforms such as DASH, SMART-NAS testbed, FACT, or FACET.
The main commercial application for the proposed technology is as a DST to be used by operational and/or analytical personnel at airlines, ANSPs, or airports, (or by aviation consultants) for analyzing root causes for observed operational efficiencies or irregularities at the end of a day of operations at key airports and airspaces. The CIDAS-P collective inference engine will enable staff to differentiate the impacts of factors under their control versus not under their control, to assist in improved operational decision-making around operational procedures, and technology and resource investments. Airline uses include better analysis of irregular operations responses, improved analysis of airline network-wide flight scheduling and management, fleet mix choices, gate turnaround, gate pushback, and non-movement area operations, diversions and cancellations, and competitive airline performance. In the case of airports, uses include the analysis of the impact of airport construction schedules, departure metering operations, and general management of gate turnaround, gate pushback and non-movement area operations. In addition, specific ANSP-focused applications include: (1) a Trajectory-based Operations (TBO) benefits analysis and monitoring capability, (2) an operational analysis tool focused on measuring the impact of ATM DSTs for departure metering, weather rerouting, and arrival metering, (3) NAS weather impact analysis tool, and (4) a post-operations TFM evaluation system.","description":"This SBIR research provides a significant improvement over current-day post operations analysis (POA) with significant commercialization potential. In Phase I, ATAC developed a machine learning based aviation POA decision support tool (DST), which improves the state of the art of today’s airline, airport and FAA POA processes by providing automated, results-oriented POA outcomes. We provided a proof-of-concept for this POA DST by demonstrating how the Phase I prototype allows airline, airport and FAA personnel at the Charlotte Douglas International Airport (CLT) to perform faster, more efficient and results-oriented post analysis of individual departure banks to obtain actionable operational insights. Encouraged by our promising proof-of-concept demonstrations in Phase I, we propose to carry forward this research in Phase II of our SBIR project towards the eventual goal of developing a commercial licensable Cloud-based POA Platform that can be accessed by NASA, FAA, airline, airport or other commercial systems or personnel in a “Platform-As-A-Service” (PAAS) mode. This proposed capability provides a one-stop platform for gate-to-gate, complete POA including aviation data acquisition, storage, analytics, and root cause diagnosis, in a post-analysis mode as well as a real-time, continuous operations monitoring mode. The proposed continuous operations monitoring mode accelerates operations analysis work related to NASA’s ATD-2 project. The proposed second airspace focused use case supports multiple NASA research programs, including ATD-2's CLT to Northeast corridor (NEC) departure flow operations analysis, IDM NEC enroute constraints analysis, ATD-3 weather-efficient routing analysis and System Wide Safety anomaly detection. Moreover, by providing the ability to perform results-oriented POA on diverse operations (UAM, IDO), the SBIR enables the future NAS to rapidly learn from operational inefficiencies, and improve new traffic management and operations paradigms.","startYear":2018,"startMonth":5,"endYear":2020,"endMonth":10,"statusDescription":"Completed","website":"","program":{"acronym":"SBIR/STTR","active":true,"description":"
The NASA SBIR and STTR programs fund the research, development, and demonstration of innovative technologies that fulfill NASA needs as described in the annual Solicitations and have significant potential for successful commercialization. If you are a small business concern (SBC) with 500 or fewer employees or a non-profit RI such as a university or a research laboratory with ties to an SBC, then NASA encourages you to learn more about the SBIR and STTR programs as a potential source of seed funding for the development of your innovations.
The SBIR and STTR programs have 3 phases:
The SBIR and STTR Phase I contracts last for 6 months with a maximum funding of $125,000, and Phase II contracts last for 24 months with a maximum funding of $750,000 - $1.5 million.
Opportunity for Continued Technology Development Post-Phase II:
The NASA SBIR/STTR Program currently has in place two initiatives for supporting its small business partners past the basic Phase I and Phase II elements of the program that emphasize opportunities for commercialization. Specifically, the NASA SBIR/STTR Program has the Phase II Enhancement (Phase II-E) and Phase II eXpanded (Phase II-X) contract options.
Please review the links below to obtain more information on the SBIR/STTR programs.
Provides an overview of the SBIR and STTR programs as implemented by NASA
Provides access to the annual SBIR/STTR Solicitations containing detailed information on the program eligibility requirements, proposal instructions and research topics and subtopics
Schedule and links for the SBIR/STTR solicitations and selection announcements
Federal and non-Federal sources of assistance for small business
Search our complete archive of awarded project abstracts to learn about what NASA has funded
Still have questions? Visit the program FAQs
","programId":73,"responsibleMd":{"acronym":"STMD","canUserEdit":false,"city":"","external":false,"linkCount":0,"organizationId":4875,"organizationName":"Space Technology Mission Directorate","organizationType":"NASA_Mission_Directorate","naorganization":false,"organizationTypePretty":"NASA Mission Directorate"},"responsibleMdId":4875,"stockImageFileId":36648,"title":"Small Business Innovation Research/Small Business Tech Transfer"},"lastUpdated":"2024-1-10","releaseStatusString":"Released","viewCount":102,"endDateString":"Oct 2020","startDateString":"May 2018"},"infoText":"Advanced within the program","infoTextExtra":"Another project within the program (Collective Inference Based Data Analytics System for Post Operations Analysis Phase II)","dateText":"May 2018"}],"primaryImage":{"file":{"fileExtension":"gif","fileId":295258,"fileSizeString":"0 Byte"},"id":291783,"description":"Collective Inference based Data Analytics System for Post Operations Analysis, Phase I Briefing Chart Image","projectId":93412,"publishedDateString":""},"responsibleMd":{"acronym":"STMD","canUserEdit":false,"city":"","external":false,"linkCount":0,"organizationId":4875,"organizationName":"Space Technology Mission Directorate","organizationType":"NASA_Mission_Directorate","naorganization":false,"organizationTypePretty":"NASA Mission Directorate"},"program":{"acronym":"SBIR/STTR","active":true,"description":"The NASA SBIR and STTR programs fund the research, development, and demonstration of innovative technologies that fulfill NASA needs as described in the annual Solicitations and have significant potential for successful commercialization. If you are a small business concern (SBC) with 500 or fewer employees or a non-profit RI such as a university or a research laboratory with ties to an SBC, then NASA encourages you to learn more about the SBIR and STTR programs as a potential source of seed funding for the development of your innovations.
The SBIR and STTR programs have 3 phases:
The SBIR and STTR Phase I contracts last for 6 months with a maximum funding of $125,000, and Phase II contracts last for 24 months with a maximum funding of $750,000 - $1.5 million.
Opportunity for Continued Technology Development Post-Phase II:
The NASA SBIR/STTR Program currently has in place two initiatives for supporting its small business partners past the basic Phase I and Phase II elements of the program that emphasize opportunities for commercialization. Specifically, the NASA SBIR/STTR Program has the Phase II Enhancement (Phase II-E) and Phase II eXpanded (Phase II-X) contract options.
Please review the links below to obtain more information on the SBIR/STTR programs.
Provides an overview of the SBIR and STTR programs as implemented by NASA
Provides access to the annual SBIR/STTR Solicitations containing detailed information on the program eligibility requirements, proposal instructions and research topics and subtopics
Schedule and links for the SBIR/STTR solicitations and selection announcements
Federal and non-Federal sources of assistance for small business
Search our complete archive of awarded project abstracts to learn about what NASA has funded
Still have questions? Visit the program FAQs
","programId":73,"responsibleMd":{"acronym":"STMD","canUserEdit":false,"city":"","external":false,"linkCount":0,"organizationId":4875,"organizationName":"Space Technology Mission Directorate","organizationType":"NASA_Mission_Directorate","naorganization":false,"organizationTypePretty":"NASA Mission Directorate"},"responsibleMdId":4875,"stockImageFileId":36648,"title":"Small Business Innovation Research/Small Business Tech Transfer"},"leadOrganization":{"canUserEdit":false,"city":"Santa Clara","congressionalDistrict":"California 17","country":{"abbreviation":"US","countryId":236,"name":"United States"},"countryId":236,"external":true,"linkCount":0,"organizationId":2805,"organizationName":"ATAC","organizationType":"Industry","stateTerritory":{"abbreviation":"CA","country":{"abbreviation":"US","countryId":236,"name":"United States"},"countryId":236,"name":"California","stateTerritoryId":59},"stateTerritoryId":59,"ein":"263189443 ","dunsNumber":"098529738","uei":"DCYJEYKZNYX5","naorganization":false,"organizationTypePretty":"Industry"},"supportingOrganizations":[{"acronym":"ARC","canUserEdit":false,"city":"Moffett Field","country":{"abbreviation":"US","countryId":236,"name":"United States"},"countryId":236,"external":false,"linkCount":0,"organizationId":4941,"organizationName":"Ames Research Center","organizationType":"NASA_Center","stateTerritory":{"abbreviation":"CA","country":{"abbreviation":"US","countryId":236,"name":"United States"},"countryId":236,"name":"California","stateTerritoryId":59},"stateTerritoryId":59,"naorganization":false,"organizationTypePretty":"NASA Center"}],"statesWithWork":[{"abbreviation":"CA","country":{"abbreviation":"US","countryId":236,"name":"United States"},"countryId":236,"name":"California","stateTerritoryId":59}],"lastUpdated":"2024-1-10","releaseStatusString":"Released","viewCount":53,"endDateString":"Dec 2017","startDateString":"Jun 2017"}}