Entity Alignment Method of Points of Interest for Internet Location-Based Services

In Internet applications, the description for the same point of interest (POI) entity for different location-based services (LBSs) is not completely identical. The POI entity information in a single LBS data source contains incomplete data and exhibits insufficient objectivity. Aligning and consolidating POI entities from various LBSs can provide users with more comprehensive, objective, and authoritative POI information. We herein propose a multi-attribute measurement-based entity alignment method for Internet LBSs to achieve POI entity alignment and data consolidation. This method is based on multi-attribute information (geographical information, text coincidence information, semantic information) of POI entities and is combined with different measurement methods to calculate the similarity of candidate entity pairs. Considering the demand for computational efficiency, the particle swarm optimization algorithm is used to train the model and optimize the weights of multi-attribute measurements. A consolidation strategy is designed for the LBS text data and user rating data from different sources to obtain more comprehensive and objective information. The experimental results show that, compared with other baseline models, the POI alignment method based on multi-attribute measurement performed the best. Using this method, the information of POI entities in multisource LBS can be integrated to serve netizens.

Getting Tired of Massive Journal Usage Statistics: A Case Study on Engineering Journal Usage Analysis Using K-Means Clustering

It would be challenging for engineering librarians who are responsible for both collection management and public service to review massive usage statistics on a regular basis. In order to tackle this challenge, we initiated a case study of measuring engineering journal usage in an alternative approach. The dataset was extracted from a data analytics company’s journal usage statistics report prepared for the University of Libraries. We decided to reuse data from their report because it would save us time in data consolidation. We segmented a total of 821 journal titles into four clusters using K-Means clustering technique where the first cluster of 38 titles with a high number of publications, citations and downloads; the second cluster of 142 titles with a low number of publications but a moderate number of citations and a high number of downloads; the third cluster of titles with a low number of publications and citations but a moderate number of downloads; the forth cluster of titles with a low number of publications, citations and downloads. In conclusion, our case study of measuring engineering journal usage converted massive journal usage statistics into four clusters of journal titles in a straightforward format. The clusters of journal titles also provided us with a comprehensive view on how engineering journals had been used by both authors and users of our institution in the most recent four years. Last but not the least, this case study showed a possibility of implementing data analytics in academic libraries.

Principles of data consolidation

Gathering data from different sources and with different cadence (sampling frequency) is normal in Precision Agriculture. However, the consolidation of those sources of information in a well structured, homogeneous database in sometimes a difficult task. In this topic, the principles of a successful data gathering and consolidation process will be explained.