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SAGEL: smart address geocoding engine for supply-chain logistics

Published: 31 October 2016 Publication History

Abstract

With the recent explosion of e-commerce industry in India, the problem of address geocoding, that is, transforming textual address descriptions to geographic reference, such as latitude, longitude coordinates, has emerged as a core problem for supply chain management. Some of the major areas that rely on precise and accurate address geocoding are supply chain fulfilment, supply chain analytics and logistics. In this paper, we present some of the challenges faced in practice while building an address geocoding engine as a core capability at Flipkart. We discuss the unique challenges of building a geocoding engine for a rapidly developing country like India, such as, fuzzy region boundaries, dynamic topography and lack of convention in spellings of toponyms, to name a few. We motivate the need for building a reliable and precise address geocoding system from a business perspective and argue why some of the commercially available solutions do not suffice for our requirements. SAGEL has evolved through 3 cycles of solution prototypes and pilot experiments. We describe the learnings from each of these phases and how we incorporated them to get to the first production-ready version. We describe how we store and index map data on a SolrCloud cluster of Apache Solr, an open-source search platform, and the core algorithm for geocoding which works post-retrieval in order to determine the best matches among a set of candidate results. We give a brief description of the system architecture and provide accuracy results of our geocoding engine by measuring deviations of geocoded customer addresses across India, from verified latitude, longitude coordinates of those addresses, for a sizeable address set. We also measure and report our system's ability to geocode up to different region levels, like city, locality or building. We compare our results with those of the geocoding service provided by Google against a set of addresses for which we have verified latitude-longitude coordinates and show that our geocoding engine is almost as accurate as Google's, while having a higher coverage.

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  • (2022)Address Location Correction System for Q-commerceProceedings of the Second International Conference on AI-ML Systems10.1145/3564121.3564800(1-7)Online publication date: 12-Oct-2022
  • (2022)CoMinerProceedings of the 30th International Conference on Advances in Geographic Information Systems10.1145/3557915.3560944(1-10)Online publication date: 1-Nov-2022
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cover image ACM Other conferences
SIGSPACIAL '16: Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
October 2016
649 pages
ISBN:9781450345897
DOI:10.1145/2996913
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 31 October 2016

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Author Tags

  1. geographic information retrieval
  2. spatial data mining and knowledge discovery
  3. spatio-textual searching
  4. storage and indexing

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SIGSPACIAL '16 Paper Acceptance Rate 40 of 216 submissions, 19%;
Overall Acceptance Rate 220 of 1,116 submissions, 20%

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Cited By

View all
  • (2024)Nationwide Behavior-Aware Coordinates Mining From Uncertain Delivery EventsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.341156236:11(6681-6698)Online publication date: Nov-2024
  • (2022)Address Location Correction System for Q-commerceProceedings of the Second International Conference on AI-ML Systems10.1145/3564121.3564800(1-7)Online publication date: 12-Oct-2022
  • (2022)CoMinerProceedings of the 30th International Conference on Advances in Geographic Information Systems10.1145/3557915.3560944(1-10)Online publication date: 1-Nov-2022
  • (2021)Deep neural network based learning to rank for address standardization2021 RIVF International Conference on Computing and Communication Technologies (RIVF)10.1109/RIVF51545.2021.9642079(1-6)Online publication date: 19-Aug-2021
  • (2020)A Geocoding Framework Powered by Delivery DataProceedings of the 28th International Conference on Advances in Geographic Information Systems10.1145/3397536.3422254(568-577)Online publication date: 3-Nov-2020
  • (2019)A Novel Conditional Random Fields Aided Fuzzy Matching in Vietnamese Address StandardizationProceedings of the 10th International Symposium on Information and Communication Technology10.1145/3368926.3369687(23-28)Online publication date: 4-Dec-2019
  • (2019)Multi Agent-Based Addresses Geocoding for More Efficient Home Delivery Service in Developing Countriese-Infrastructure and e-Services for Developing Countries10.1007/978-3-030-16042-5_26(294-304)Online publication date: 22-Mar-2019
  • (2017)Effective Scalable and Integrative Geocoding for Massive Address DatasetsProceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems10.1145/3139958.3139986(1-10)Online publication date: 7-Nov-2017

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