OpenGeoBase: Information Centric Networking Meets Spatial Database Applications

Abstract

This paper explores methodologies, advantages and challenges related to the use of Information Centric Networking (ICN) for realizing distributed spatial databases. Our findings show that the ICN functionality perfectly fits database requirements: routing-by-name can be used to dispatch queries and insertions, in-network caching to accelerate queries, and datacentric security to implement secure multi-tenancy. We present an ICN-based distributed spatial database, named OpenGeoBase, and describe its design choices. Thanks to ICN, OpenGeoBase can quickly and efficiently provide information to database users; easily operate in a distributed way, using many database engines in parallel; secure every piece of content; slice resources, so that several tenants and users can concurrently and independently use the database. We also show how OpenGeoBase can support a real world Intelligent Transport System application, by enabling discovery of geo-referenced public transportation information.

Federation and Orchestration: A Scalable Solution for EU Multimodal Travel Information Services

“…The database we designed for the NAP system is the first one that uses ICN technology for federation purposes. In [22,26] we presented a "distributed" database based on ICN; here we have a new federated version of it that best fits the NAP scenario; specifically: stakeholders (rather than an uncontrolled global "sharding" function) decide where to store data (i.e., which NAP is trusted); there are different administrators (e.g., one per member state) rather than a single one; the new query routing approach is much more efficient, making it possible to submit just a single query per database site in order to solve spatial queries of any size, and more. With respect to other stand-alone NoSQL databases, e.g., MongoDB, Table 1 reports on some features of the Bonvoyage DB that make it tailored for NAP applications.…”

“…For instance in Figure 2a, the orchestrator carries out a discovery query whose area intersects both Italy and Norway; the receiving Norway (NO) NAP forwards the query to the Italy (IT) NAP too, then collects internal Norwegian and external Italian results, validates them through embedded security bits, and sends the result back to the orchestrator (not shown). As in [22], ICN caching is used to directly reply to queries of popular objects, reducing query latency and offloading the back-end NoSQL base.…”

Applications of geospatial big data in the Internet of Things

“…The complex structure and volume of geospatial data hinder their retrieval and manipulation (Gaede & Günther, 1998) and require some special procedures in their preparation and processing (Jo & Lee, 2019). The treatment of coordinates demands complex preprocessing operations, such as transformation in geometry; conversion between standards, formats, and coordinate systems; and evaluation of spatial relationships between objects (Detti, Melazzi, Orru, Paolillo, & Rossi, 2016;Jo & Lee, 2019). This makes the computational cost of geospatial operations potentially higher than that of the usual relational operators.…”

“…Typically, these are implemented as extensions for database management systems that already exist (Detti et al, 2016). However, the rise in the geospatial data volume and generation speed makes it challenging to use traditional information systems.…”

“…Hadoop works with SpatialHadoop (Eldawy, 2014), while Spark is complemented by GeoSpark (currently Apache Sedona; Yu, Wu, & Sarwat, 2015). MongoDB, BigTable, Cassandra, and CouchDB also provide spatial support (Detti et al, 2016).…”