A Generic and Extensible Core and Prototype of Consistent, Distributed, and Resilient LIS

The majority of the existing land information systems (LIS) are centralized, transaction processing systems based on object-relational database management systems for data storage, management, and retrieval. These traditional database management systems are dominantly based on a share-everything or share disk architecture and face challenges in meeting the performance and scalability requirements of distributed, data-intensive systems, including LIS. They support vertical, rather than horizontal scalability, which is of particular importance in distributed systems. In some cases, due to legal, administrative, or infrastructure constraints, LIS need to be distributed rather than centralized systems. Distributed computing systems and share-nothing architecture have become very popular, including new data processing platforms and frameworks with horizontal scalability and fault tolerance capabilities. In this paper, we present cdrLIS—a generic and extensible core of LIS based on relevant international standards and the NewSQL database management system (DBMS) that enables the implementation of consistent, distributed, highly-available, and resilient LIS. A generic core is implemented in the Go programming language and can be easily extended and adopted towards the implementation of a specific country profile. cdrLIS can be deployed either on a computer cluster or on cloud computing platforms and thus support the design and building of a new generation of distributed and resilient data-intensive applications and information systems in the land administration domain.

[1]  Christopher Frost,et al.  Spanner: Google's Globally-Distributed Database , 2012, OSDI.

[2]  Zdravko Galić,et al.  Distributed processing of big mobility data as spatio-temporal data streams , 2016, GeoInformatica.

[3]  Jia Yu,et al.  Spatial data management in apache spark: the GeoSpark perspective and beyond , 2018, GeoInformatica.

[4]  Michael J. Cahill.  Serializable isolation for snapshot databases , 2009, TODS.

[5]  Michael Stonebraker,et al.  SciDB DBMS Research at M.I.T , 2013, IEEE Data Eng. Bull..

[6]  Ahmed Eldawy,et al.  SpatialHadoop: A MapReduce framework for spatial data , 2015, 2015 IEEE 31st International Conference on Data Engineering.

[7]  Kevin Waugh,et al.  A Classification of Object-Relational Impedance Mismatch , 2009, 2009 First International Confernce on Advances in Databases, Knowledge, and Data Applications.

[8]  Liu Chen,et al.  A Survey on NoSQL Stores , 2018, ACM Comput. Surv..

[9]  Michael Stonebraker,et al.  New opportunities for New SQL , 2012, CACM.

[10]  Marko Vukolic,et al.  Consistency in Non-Transactional Distributed Storage Systems , 2015, ACM Comput. Surv..

[11]  Alberto Belussi,et al.  Skewness-Based Partitioning in SpatialHadoop , 2020, ISPRS Int. J. Geo Inf..

[12]  Andrew S. Tanenbaum,et al.  A brief introduction to distributed systems , 2016, Computing.

[13]  Robbert van Renesse,et al.  Paxos Made Moderately Complex , 2015, ACM Comput. Surv..

[14]  Andrew Pavlo,et al.  What's Really New with NewSQL? , 2016, SGMD.

[15]  Seif Haridi,et al.  Apache Flink™: Stream and Batch Processing in a Single Engine , 2015, IEEE Data Eng. Bull..

[16]  Leslie Lamport,et al.  The part-time parliament , 1998, TOCS.

[17]  Rohan Mark Bennett,et al.  Transformations, transitions, or tall tales? A global review of the uptake and impact of NoSQL, blockchain, and big data analytics on the land administration sector , 2019, Land Use Policy.

[18]  Branislav Bajat,et al.  Prototype of the 3D Cadastral System Based on a NoSQL Database and a JavaScript Visualization Application , 2019, ISPRS Int. J. Geo Inf..

[19]  Rohan Bennett,et al.  The land administration domain model , 2015 .

[20]  Alfons Kemper,et al.  HyPer: A hybrid OLTP&OLAP main memory database system based on virtual memory snapshots , 2011, 2011 IEEE 27th International Conference on Data Engineering.