Relevant bibliographies by topics / Data warehousing

Academic literature on the topic 'Data warehousing'

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Published: 4 June 2021
Last updated: 30 July 2024

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Journal articles on the topic "Data warehousing"

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Jaroli, Priyanka, and Palak Masson. "Data Warehousing and OLAP Technology (Data warehousing)." International Journal of Engineering Trends and Technology 51, no. 1 (September 25, 2017): 45–50. http://dx.doi.org/10.14445/22315381/ijett-v51p208.

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Griffin, Robert K. "Data Warehousing." Cornell Hotel and Restaurant Administration Quarterly 39, no. 4 (August 1998): 28–35. http://dx.doi.org/10.1177/001088049803900406.

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Hoven, John van den. "Data Warehousing." Information Systems Management 14, no. 1 (January 1997): 70–72. http://dx.doi.org/10.1080/10580539708907035.

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Daniels, Shirley. "Data warehousing." Work Study 44, no. 7 (November 1995): 4–5. http://dx.doi.org/10.1108/00438029510096526.

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Cole, Karen, Michael Somers, and Jill Emery. "Data Warehousing." Serials Librarian 40, no. 3-4 (June 20, 2001): 349–53. http://dx.doi.org/10.1300/j123v40n03_22.

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Kumar Madria, Sanjay. "Data warehousing." Data & Knowledge Engineering 39, no. 3 (December 2001): 215–17. http://dx.doi.org/10.1016/s0169-023x(01)00040-4.

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Ferguson, Neil. "Data Warehousing." International Review of Law, Computers & Technology 11, no. 2 (October 1997): 243–50. http://dx.doi.org/10.1080/13600869755686.

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Tan, Xin, David C. Yen, and Xiang Fang. "Web warehousing: Web technology meets data warehousing." Technology in Society 25, no. 1 (January 2003): 131–48. http://dx.doi.org/10.1016/s0160-791x(02)00061-1.

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S., ARUNACHALAM, PAGE TOM, and THORSTEINSSON G. "Healthcare Data Warehousing." i-manager's Journal on Computer Science 4, no. 4 (2017): 1. http://dx.doi.org/10.26634/jcom.4.4.13414.

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Alshawi, Sarmad. "Oracle8i Data Warehousing." European Journal of Information Systems 10, no. 1 (March 2001): 67. http://dx.doi.org/10.1057/palgrave.ejis.3000356.

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Dissertations / Theses on the topic "Data warehousing"

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Ghrab, Amine. "Graph data warehousing." Doctoral thesis, Universitat Politècnica de Catalunya, 2020. http://hdl.handle.net/10803/672454.

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Over the last decade, we have witnessed the emergence of networks in a wide spectrum of application domains, ranging from social and information networks to biological and transportation networks. Graphs provide a solid theoretical foundation for modeling complex networks, and revealing valuable insights from both the network structure and the data embedded within its entities. As the business and social environments are getting increasingly complex and interconnected, graphs became a widespread abstraction at the core of the information infrastructure supporting those environments. Modern information systems consist of a large number of sophisticated and interacting business entities that naturally form graphs. In particular, integrating graphs into data warehouse systems received a lot of interest from both academia and industry. Indeed, data warehouses are the central enterprise's information repository, and are critical for proper decision support and future planning. Graph warehousing is emerging as the field that extends current information systems with graph management and analytics capabilities. Many approaches were proposed to address the graph data warehousing challenge. These efforts laid the foundation for multidimensional modeling and analysis of graphs. However, most of the proposed approaches partially tackle the graph warehousing problem by being restricted to simple abstractions such as homogeneous graphs or ignoring important topics such as multidimensional integrity constraints and dimension hierarchies. In this dissertation, we conduct a systematic study of the graph data warehousing topic, and address the key challenges of database and multidimensional modeling of graphs. We first propose GRAD, a new graph database model specifically tuned for warehousing and OLAP analytics. GRAD aims to provide analysts with a set of simple, well-defined, and adaptable conceptual components to support rich semantics and perform complex analysis on graphs. Then, we define the multidimensional concepts for heterogeneous attributed graphs and highlight the new types of measures that could be derived. We project this multidimensional model on property graphs and explore how to extract the candidate multidimensional concepts and build graph cubes. Then, we extend the multidimensional model by integrating GRAD and show how graph modeling based on GRAD facilitates multidimensional modeling, and enables supporting dimension hierarchies and building new types of OLAP cubes on graphs. Afterwards, we present TopoGraph, a graph data warehousing framework that extends current graph warehousing models with new types of cubes and queries combining graph-oriented and OLAP querying. TopoGraph goes beyond traditional OLAP cubes, which process value-based grouping of tables, by considering in addition the topological properties of the graph elements. And it goes beyond current graph warehousing models by proposing new types of graph cubes. These cubes embed a rich repertoire of measures that could be represented with numerical values, with entire graphs, or as a combination of them. Finally, we propose an architecture of the graph data warehouse and describe its main building blocks and the remaining gaps. The various components of the graph warehousing framework can be effectively leveraged as a foundation for designing and building industry-grade graph data warehouses. We believe that our research in this thesis brings us a step closer towards a better understanding of graph warehousing. Yet, the models and framework we proposed are the tip of the iceberg. The marriage of graph and warehousing technologies will bring many exciting research opportunities, which we briefly discuss at the end of the thesis.
Durant l’última dècada, hem estat testimonis de l’aparició de xarxes en un ampli espectre de dominis d’aplicació, que van de les xarxes socials i d’informació a xarxes biològiques i de transport. Els grafs proporcionen un fonament teòric sòlid per a modelar xarxes complexes i revelen informació valuosa tant de l'estructura de la xarxa com de les dades integrades a les seves entitats. A mesura que els entorns empresarials i socials són cada cop més complexos i interconnectats, els grafs es van convertir en una abstracció generalitzada en el nucli de la infraestructura d'informació que dona suport a aquests entorns. Els sistemes d'informació moderns consisteixen en un gran nombre d'entitats empresarials i la seva interacció, que formen grafs de forma natural. En particular, la integració de grafs en sistemes de magatzem de dades va rebre molt d’interès tant de l’àmbit acadèmic com de la indústria. De fet, els magatzems de dades són el repositori central d'informació de l'empresa i són fonamentals per a un suport adequat a la presa de decisions i una planificació futura. Els magatzems de dades en graf (graph data warehousing) és un camp emergent que estén els sistemes d’informació tradicionals amb capacitats d’administració i d’anàlisi de dades en format grafs. Fins ara, s'han proposat molts enfocaments per afrontar el repte de l'emmagatzematge de dades en graf. Aquests esforços van posar els fonaments pel modelatge i l'anàlisi de grafs d'una perspectiva multidimensional. Tanmateix, la majoria dels plantejaments proposats aborden parcialment el problema de l'emmagatzematge de grafs restringint-se a abstraccions simples com ara grafs homogenis o ignorant temes importants com ara restriccions d’integritat multidimensionals i jerarquies de dimensió. En aquesta tesi realitzem un estudi sistemàtic del tema d'emmagatzematge de dades en graf i tractem els reptes clau de la base de dades i el modelatge multidimensional de grafs. Primer proposem GRAD, un nou model de base de dades de grafs específicament ajustat per a emmagatzematge i analítica OLAP. GRAD pretén proporcionar als analistes un conjunt de components conceptuals simples, ben definits i adaptables per donar suport a elements semàntics complexos i realitzar anàlisis complexos sobre grafs. A continuació, definim els conceptes multidimensionals per a grafs heterogenis amb atributs i ressaltem els nous tipus de mesures que es poden derivar. Projectem aquest model multidimensional en property graphs i explorem com extreure conceptes multidimensionals candidats i construir cubs de grafs. A continuació, ampliem el model multidimensional integrant GRAD i mostrem com el modelatge de grafs basat en GRAD facilita el modelatge multidimensional i permet suportar jerarquies de dimensions i crear nous tipus de cubs OLAP en grafs. Després, presentem TopoGraph, un marc d’emmagatzematge de dades en graf que amplia els models d’emmagatzematge de grafs actuals amb nous tipus de cubs i consultes que combinen la consulta orientada a grafs i OLAP. TopoGraph va més enllà dels cubs tradicionals OLAP, que processen l'agrupació de taules basada en el valor, considerant a més les propietats topològiques dels grafs. I va més enllà dels models d’emmagatzematge en graf actuals proposant nous tipus de cubs de grafs. Aquests cubs incorporen un ric repertori de mesures que es podrien representar amb valors numèrics, amb grafs sencers o com a combinació d’aquests. Finalment, proposem una arquitectura per al magatzem de dades en graf i descrivim els blocs de construcció principals i els buits restants. Els diversos components del marc d'emmagatzematge de grafs es poden aprofitar eficaçment com a base per dissenyar i construir magatzems de dades de grafs a nivell industrial. Creiem que la nostra recerca en aquesta tesi ens apropa un pas més cap a una millor comprensió de graph warehousing.
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Gehrke, Christian. "Informationsagenten im Data Warehousing /." Heidelberg : Physica-Verlag, 2000. http://aleph.unisg.ch/hsgscan/hm00015380.pdf.

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Oladele, Kazeem Ayinde. "Investigating pluralistic data architectures in data warehousing." Thesis, Brunel University, 2015. http://bura.brunel.ac.uk/handle/2438/10534.

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Understanding and managing change is a strategic objective for many organisations to successfully compete in a market place; as a result, organisations are leveraging their data asset and implementing data warehouses to gain business intelligence necessary to improve their businesses. Data warehouses are expensive initiatives, one-half to two-thirds of most data warehousing efforts end in failure. In the absence of well-formalised design methodology in the industry and in the context of the debate on data architecture in data warehousing, this thesis examines why multidimensional and relational data models define the data architecture landscape in the industry. The study develops a number of propositions from the literature and empirical data to understand the factors impacting the choice of logical data model in data warehousing. Using a comparative case study method as the mean of collecting empirical data from the case organisations, the research proposes a conceptual model for logical data model adoption. The model provides a framework that guides decision making for adopting a logical data model for a data warehouse. The research conceptual model identifies the characteristics of business requirements and decision pathways for multidimensional and relational data warehouses. The conceptual model adds value by identifying the business requirements which a multidimensional and relational logical data model is empirically applicable.
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張振隆 and Chun-lung Cheung. "Data warehousing mobile code design." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2000. http://hub.hku.hk/bib/B29872996.

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Cheung, Chun-lung. "Data warehousing mobile code design." Hong Kong : University of Hong Kong, 2000. http://sunzi.lib.hku.hk/hkuto/record.jsp?B23001057.

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Fan, Hao. "Investigating a heterogeneous data integration approach for data warehousing." Thesis, Birkbeck (University of London), 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.424299.

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Chen, Songting. "Efficient incremental view maintenance for data warehousing." Link to electronic thesis, 2005. http://www.wpi.edu/Pubs/ETD/Available/etd-122005-193617/.

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Dissertation (Ph.D.)--Worcester Polytechnic Institute.
Keywords: View Matching; View Maintenance; Materialized View; Data Warehouse; Information Integration. Includes bibliographical references. (p.206-215)
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Haak, Liane. "Semantische Integration von Data Warehousing und Wissensmanagement /." Berlin : Dissertation.de - Verl. im Internet, 2008. http://d-nb.info/989917010/04.

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To, Cho-ying Joanne, and 杜祖鸚. "Planning and strategic application of data warehousing." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1998. http://hub.hku.hk/bib/B3126928X.

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Haak, Liane. "Semantische Integration von Data Warehousing und Wissensmanagement." Berlin dissertation.de, 2007. http://d-nb.info/989917010/04.

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Books on the topic "Data warehousing"

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Thareja, Reema. Data warehousing. New Delhi: Oxford University Press, 2009.

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Author, Hammergren Thomas C., ed. Data Warehousing. 2nd ed. Hoboken: For Dummies [Imprint], 2009.

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Jung, Reinhard, and Robert Winter, eds. Data Warehousing Strategie. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-58350-6.

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Jung, Reinhard, and Robert Winter, eds. Data Warehousing 2000. Heidelberg: Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-642-57681-2.

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Dodge, Gary. Oracle8 data warehousing. New York: Wiley, 1998.

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Corey, Michael J. Oracle data warehousing. Berkeley, Calif: Osborne McGraw-Hill, 1997.

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J, Corey Michael, ed. Oracle8i data warehousing. New York: Osborne/McGraw-Hill, 2001.

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Singh, Harry. Interactive data warehousing. Upper Saddle River, NJ: Prentice Hall PTR, 1999.

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J, Corey Michael, ed. Oracle8 data warehousing. New York: McGraw-Hill, 1998.

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1962-, Gorman Tim, ed. Oracle8 data warehousing. New York: Wiley, 1998.

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Book chapters on the topic "Data warehousing"

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Lusti, Markus. "Data Warehousing." In Data Warehousing und Data Mining, 129–258. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-56033-0_5.

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Lusti, Markus. "Data Warehousing." In Data Warehousing und Data Mining, 123–248. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-97747-3_5.

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Foster, Elvis C., and Shripad Godbole. "Data Warehousing." In Database Systems, 377–90. Berkeley, CA: Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-1191-5_19.

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Fischer, Joachim. "Data Warehousing." In Handbuch Digitale Wirtschaft, 511–16. Wiesbaden: Springer Fachmedien Wiesbaden, 2020. http://dx.doi.org/10.1007/978-3-658-17291-6_39.

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Fischer, Joachim. "Data Warehousing." In Handbuch Digitale Wirtschaft, 1–6. Wiesbaden: Springer Fachmedien Wiesbaden, 2018. http://dx.doi.org/10.1007/978-3-658-17345-6_39-1.

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Johnson, Theodore. "Data Warehousing." In Massive Computing, 661–710. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-0005-6_19.

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Gray, Paul. "Data Warehousing." In Encyclopedia of Operations Research and Management Science, 362–67. Boston, MA: Springer US, 2013. http://dx.doi.org/10.1007/978-1-4419-1153-7_1180.

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Foster, Elvis C., and Shripad V. Godbole. "Data Warehousing." In Database Systems, 387–401. Berkeley, CA: Apress, 2014. http://dx.doi.org/10.1007/978-1-4842-0877-9_24.

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Beynon-Davies, Paul. "Data Warehousing." In Database Systems, 527–38. London: Macmillan Education UK, 2004. http://dx.doi.org/10.1007/978-0-230-00107-7_40.

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Shekhar, Shashi, and Hui Xiong. "Data Warehousing." In Encyclopedia of GIS, 225. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_255.

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Conference papers on the topic "Data warehousing"

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Golab, Lukasz, and Theodore Johnson. "Data stream warehousing." In the 2013 international conference. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2463676.2465337.

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Golab, Lukasz, and Theodore Johnson. "Data stream warehousing." In 2014 IEEE 30th International Conference on Data Engineering (ICDE). IEEE, 2014. http://dx.doi.org/10.1109/icde.2014.6816763.

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Dubey, K. Kiran, and Devidas Kalwale. "Data warehousing techniques." In the International Conference & Workshop. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/1980022.1980385.

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Golfarelli, Matteo, Stefano Rizzi, and Iuris Cella. "Beyond data warehousing." In the 7th ACM international workshop. New York, New York, USA: ACM Press, 2004. http://dx.doi.org/10.1145/1031763.1031765.

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Santos, Maribel Yasmina, and Carlos Costa. "Data Warehousing in Big Data." In the Ninth International C* Conference. New York, New York, USA: ACM Press, 2016. http://dx.doi.org/10.1145/2948992.2949024.

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Karlapalem, Kamalakar. "Beyond Data Warehousing→Data Logistics." In 9th IFIP 2.6 Working Conference on Database Semantics (DS-9). WORLD SCIENTIFIC, 2001. http://dx.doi.org/10.1142/9789812838995_0003.

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Casati, Fabio, Malu Castellanos, Norman Salazar, and Umeshwar Dayal. "Abstract Process Data Warehousing." In 2007 IEEE 23rd International Conference on Data Engineering. IEEE, 2007. http://dx.doi.org/10.1109/icde.2007.369018.

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Boreisha, Yurii. "Internet-based data warehousing." In Intelligent Systems and Advanced Manufacturing, edited by Angappa Gunasekaran and Yahaya Y. Yusuf. SPIE, 2001. http://dx.doi.org/10.1117/12.443132.

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Shahzad, Muhammad A. "Data warehousing with Oracle." In AeroSense '99, edited by Belur V. Dasarathy. SPIE, 1999. http://dx.doi.org/10.1117/12.339986.

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Winsemann, Thorsten, and Veit Koppen. "Persistence in Data Warehousing." In 2012 Sixth International Conference on Research Challenges in Information Science (RCIS). IEEE, 2012. http://dx.doi.org/10.1109/rcis.2012.6240466.

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Reports on the topic "Data warehousing"

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Warner, Neil. Information Superiority through Data Warehousing. Fort Belvoir, VA: Defense Technical Information Center, June 2001. http://dx.doi.org/10.21236/ada468487.

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Musick, R., T. Critchlow, M. Ganesh, Z. Fidelis, A. Zemla, and T. Slezak. Data Foundry: Data Warehousing and Integration for Scientific Data Management. Office of Scientific and Technical Information (OSTI), February 2000. http://dx.doi.org/10.2172/793555.

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Neely, M. P. Data Quality Tools for Data Warehousing - A Small Sample Survey. Fort Belvoir, VA: Defense Technical Information Center, January 1998. http://dx.doi.org/10.21236/ada362923.

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Widom, Jennifer. A Warehousing Approach to Data and Knowledge Integration. Fort Belvoir, VA: Defense Technical Information Center, August 1997. http://dx.doi.org/10.21236/ada329436.

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Chandra, Shailesh, Aastha Chaudhary, Prakhar Srivastava, and Jose Torres-Aguilera. Evaluating Automated Truck Platoon (ATP) Deployment for the Los Angeles–Inland Empire Trade Corridor Enhancement. Mineta Transportation Institute, March 2023. http://dx.doi.org/10.31979/mti.2023.2244.

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The California Freight Mobility Plan 2020 lists the Los Angeles-Inland Empire trade corridor region as a prominent industrial hub experiencing an increase in freight flows. The California Freight Mobility Plan also regards automated truck platoon (ATP) as an emerging opportunity to minimize congestion on the trade corridor routes. Percentage change in accessibility from 2022 (“without” ATP) to 2040 (“with” ATP) is calculated for the eighteen industry sectors of the Los Angeles-Inland Empire trade corridor. The application of the accessibility formulation was carried out with data on travel time from I-710 and I-10 within Los Angeles County. The findings suggest that all the industry sectors have a very high positive percentage change in accessibility by transforming from “without” to “with” ATP deployment-based accessibility. In the vicinity of the prominent freight corridors of I-710 and I-10 within Los Angeles County, notably, the largest increase in accessibility above 90% will be observed for the industry sectors of Agriculture, Forestry, Fishing and Hunting, Health Care and Social Assistance, Finance and Insurance, Transportation and Warehousing, and Retail Trade of the Los-Angeles-Inland Empire. Thus, these findings suggest the deployment of ATP on specific freight routes to enhance and sustain economic activity across the Los Angeles-Inland Empire trade corridors.
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