Esteban Zimanyi | Searcharxiv

arXiv2025

Mobility Stream Processing on NebulaStream and MEOS

The increasing use of Internet-of-Things (IoT) sensors in moving objects has resulted in vast amounts of spatiotemporal streaming data. To analyze this data in situ, real-time spatiotemporal processing is needed. However, current stream processing systems designed for IoT environments often lack spatiotemporal processing capabilities, and existing spatiotemporal libraries primarily focus on analyzing historical data. This gap makes performing real-time spatiotemporal analytics challenging. In this demonstration, we present NebulaMEOS, which combines MEOS (Mobility Engine Open Source), a spatiotemporal processing library, with NebulaStream, a scalable data management system for IoT applications. By integrating MEOS into NebulaStream, NebulaMEOS utilizes spatiotemporal functionalities to process and analyze streaming data in real-time. We demonstrate NebulaMEOS by querying data streamed from edge devices on trains by the Société Nationale des Chemins de fer Belges (SNCB). Visitors can experience demonstrations of geofencing and geospatial complex event processing, visualizing real-time train operations and environmental impacts.

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arXiv2018

Evaluation of Semantic Metadata Pair Modelling Using Data Clustering

Metadata presents a medium for connection, elaboration, examination, and comprehension of relativity between two datasets. Metadata can be enriched to calculate the existence of a connection between different disintegrated datasets. In order to do so, the very first task is to attain a generic metadata representation for domains. This representation narrows down the metadata search space. The metadata search space consists of attributes, tags, semantic content, annotations etc. to perform classification. The existing technologies limit the metadata bandwidth i.e. the operation set for matching purposes is restricted or limited. This research focuses on generating a mapper function called cognate that can find mathematical relevance based on pairs of attributes between disintegrated datasets. Each pair is designed from one of the datasets under consideration using the existing metadata and available meta-tags. After pairs have been generated, samples are constructed using a different combination of pairs. The similarity and relevance between two or more pairs are attained by using a data clustering technique to generate large groups from smaller groups based on similarity index. The search space is divided using a domain divider function and smaller search spaces are created using relativity and tagging as the main concept. For this research, the initial datasets have been limited to textual information. Once all disjoint meta-collection have been generated the approximation algorithm calculates the centers of each meta-set. These centers serve the purpose of meta-pointers i.e. a collection of meta-domain representations. Each pointer can then join a cluster based on the content i.e. meta-content. It also facilitates the process of possible synonyms across cross-functional domains. This can be examined using meta-pointers and graph pools.

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