Complementing information about particular points, places, or institutions, i.e., so-called Points of Interest (POIs) can be achieved by matching data from the growing number of geospatial databases; these include Foursquare, OpenStreetMap, Yelp, and Facebook Places. Doing this potentially allows for the acquisition of more accurate and more complete information about POIs than would be possible by merely extracting the information from each of the systems alone. Problem: The task of Points of Interest matching, and the development of an algorithm to perform this automatically, are quite challenging problems due to the prevalence of different data structures, data incompleteness, conflicting information, naming differences, data inaccuracy, and cultural and language differences; in short, the difficulties experienced in the process of obtaining (complementary) information about the POI from different sources are due, in part, to the lack of standardization among Points of Interest descriptions; a further difficulty stems from the vast and rapidly growing amount of data to be assessed on each occasion. Research design and contributions: To propose an efficient algorithm for automatic Points of Interest matching, we: (1) analyzed available data sources—their structures, models, attributes, number of objects, the quality of data (number of missing attributes), etc.—and defined a unified POI model; (2) prepared a fairly large experimental dataset consisting of 50,000 matching and 50,000 non-matching points, taken from different geographical, cultural, and language areas; (3) comprehensively reviewed metrics that can be used for assessing the similarity between Points of Interest; (4) proposed and verified different strategies for dealing with missing or incomplete attributes; (5) reviewed and analyzed six different classifiers for Points of Interest matching, conducting experiments and follow-up comparisons to determine the most effective combination of similarity metric, strategy for dealing with missing data, and POIs matching classifier; and (6) presented an algorithm for automatic Points of Interest matching, detailing its accuracy and carrying out a complexity analysis. Results and conclusions: The main results of the research are: (1) comprehensive experimental verification and numerical comparisons of the crucial Points of Interest matching components (similarity metrics, approaches for dealing with missing data, and classifiers), indicating that the best Points of Interest matching classifier is a combination of random forest algorithm coupled with marking of missing data and mixing different similarity metrics for different POI attributes; and (2) an efficient greedy algorithm for automatic POI matching. At a cost of just 3.5% in terms of accuracy, it allows for reducing POI matching time complexity by two orders of magnitude in comparison to the exact algorithm.

JavaScript Object Notation was originally designed to transfer data; however, it soon found another use as a way of persisting data in NoSQL databases. Recently, the most-popular relational databases have introduced JSON as a native column type, which makes it easier to store and query dynamic database schema. In this paper, we review the currently popular techniques of storing data with a dynamic model with a large number of relationships between entities in relational databases. We focus on creating a simple dynamic schema with JSON in the most-popular relational databases, and we compare it with the well-known EAV data model and the document database. The results of precisely selected tests in the field of criminal data suggest that the use of JSON in dynamic database schema greatly simplifies queries and reduces their execution time compared to the widely used approaches.

Model for Dynamic and Hierarchical Data Repository in Relational Database

Semi-structural data tend to be problematic due to the sparsity of their attributes and due to the fact that, regardless of their type, they are immensely diverse. This means that data storage is a challenge, especially when the data contained within a relational database – often a strict requirement defined in advance. In this paper, we present a thoroughly described concept of a repository that is capable of storing and processing semi-structural data. Based on this concept, we establish a database model comprising the architecture and the tools needed to search the data and build relevant processors. The processor described may assign roles and dispatch tasks between the users. We demonstrate how the capacities of this repository are capable of overcoming current limitations by creating a system for facilitated digitization of scientific resources. In addition, we show that the repository in question is suitable for general use, and, as such, may be adapted to any domains in which semi-structural data are processed, without any additional work required

Point of Interest (POI) is a general term for objects describing places from the real world. The concept of POIs matching, i.e. determining whether two sets of attributes represent the same location, is not a trivial challenge due to the large variety of data sources. The representation of POIs may vary depending on the base in which they are stored. Manual comparison of objects with each other is not achievable in real-time, therefore there are multiple solutions to automatic merging. However there is no efficient solution that includes the deficiencies in the existence of attributes, has been proposed so far. In this paper, we propose the Multilayered Hybrid Classifier which is composed of machine learning and deep learning techniques, supported by the first-past-the-post voting system. We examined different weights for constituencies which were taken into consideration during the majority (or supermajority) decision. As a result, we achieved slightly higher accuracy than the current best model - Random Forest, which in its working also base on voting.