Geo-Social Group Queries with Minimum Acquaintance Constraint

The prosperity of location-based social networking services enables geo-social group queries for group-based activity planning and marketing. This paper proposes a new family of geo-social group queries with minimum acquaintance constraint (GSGQs), which are more appealing than existing geo-social group queries in terms of producing a cohesive group that guarantees the worst-case acquaintance level. GSGQs, also specified with various spatial constraints, are more complex than conventional spatial queries; particularly, those with a strict $k$NN spatial constraint are proved to be NP-hard. For efficient processing of general GSGQ queries on large location-based social networks, we devise two social-aware index structures, namely SaR-tree and SaR*-tree. The latter features a novel clustering technique that considers both spatial and social factors. Based on SaR-tree and SaR*-tree, efficient algorithms are developed to process various GSGQs. Extensive experiments on real-world Gowalla and Dianping datasets show that our proposed methods substantially outperform the baseline algorithms based on R-tree.Comment: This is the preprint version that is accepted by the Very Large Data Bases Journa

Utilizing Internet Big Data and Machine Learning for Product Demand Forecasting and Analysis of Its Economic Benefits

In the context of digitalization and big data-driven advancements, the accuracy of demand forecasting in supply chain management has become a key competitive factor for businesses. This paper introduces a hybrid model combining Graph Convolutional Networks (GCN), Long Short-Term Memory networks (LSTM), and attention mechanisms, which enhances forecasting performance by integrating internet big data. The model extracts key information from multiple data sources, uses GCN to capture complex relationships within the supply chain, and employs LSTM for processing time-series data, while the attention mechanism boosts sensitivity to critical time points and relationships, significantly improving prediction accuracy. Moreover, the model optimizes production plans and inventory management, reduces the risk of supply chain disruptions, and enhances market adaptability and competitiveness

Processing Spatial Keyword Query as a Top-k Aggregation Query

We examine the spatial keyword search problem to retrieve objects of interest that are ranked based on both their spatial proximity to the query location as well as the textual relevance of the object’s keywords. Existing solutions for the problem are based on either using a combination of textual and spatial indexes or using specialized hybrid indexes that integrate the indexing of both textual and spatial attribute values. In this paper, we propose a new approach that is based on modeling the problem as a top-k aggregation problem which enables the design of a scalable and efficient solution that is based on the ubiquitous inverted list index. Our performance study demonstrates that our approach outperforms the state-of-theart hybrid methods by a wide margin

From Keyword Search to Exploration: How Result Visualization Aids Discovery on the Web

A key to the Web's success is the power of search. The elegant way in which search results are returned is usually remarkably effective. However, for exploratory search in which users need to learn, discover, and understand novel or complex topics, there is substantial room for improvement. Human computer interaction researchers and web browser designers have developed novel strategies to improve Web search by enabling users to conveniently visualize, manipulate, and organize their Web search results. This monograph offers fresh ways to think about search-related cognitive processes and describes innovative design approaches to browsers and related tools. For instance, while key word search presents users with results for specific information (e.g., what is the capitol of Peru), other methods may let users see and explore the contexts of their requests for information (related or previous work, conflicting information), or the properties that associate groups of information assets (group legal decisions by lead attorney). We also consider the both traditional and novel ways in which these strategies have been evaluated. From our review of cognitive processes, browser design, and evaluations, we reflect on the future opportunities and new paradigms for exploring and interacting with Web search results