7,088 research outputs found
Spatial Queries for Indoor Location-based Services
Indoor Location-based Services (LBS) facilitate people in indoor scenarios such as airports, train stations, shopping malls, and office buildings. Indoor spatial queries are the foundation to support indoor LBSs. However, the existing techniques for indoor spatial queries are limited to support more advanced queries that consider semantic information, temporal variations, and crowd influence. This work studies indoor spatial queries for indoor LBSs. Some typical proposals for indoor spatial queries are compared theoretically and experimentally. Then, it studies three advanced indoor spatial queries, a) Indoor Keyword-aware Routing Query. b) Indoor Temporal-variation aware Routing Query. c) Indoor Crowd-aware Routing Query. A series of techniques are proposed to solve these problems.</p
Combination of content analysis and context features for digital photograph retrieval.
In recent years digital cameras have seen an enormous rise
in popularity, leading to a huge increase in the quantity of
digital photos being taken. This brings with it the challenge of organising these large collections. The MediAssist project uses date/time and GPS location for the
organisation of personal collections. However, this context
information is not always sufficient to support retrieval
when faced with a large, shared, archive made up of
photos from a number of users. We present work in this
paper which retrieves photos of known objects (buildings,
monuments) using both location information and content-based
retrieval tools from the AceToolbox. We show that
for this retrieval scenario, where a user is searching for
photos of a known building or monument in a large shared
collection, content-based techniques can offer a significant
improvement over ranking based on context (specifically
location) alone
Towards Crowd-aware Indoor Path Planning (Extended Version)
Indoor venues accommodate many people who collectively form crowds. Such
crowds in turn influence people's routing choices, e.g., people may prefer to
avoid crowded rooms when walking from A to B. This paper studies two types of
crowd-aware indoor path planning queries. The Indoor Crowd-Aware Fastest Path
Query (FPQ) finds a path with the shortest travel time in the presence of
crowds, whereas the Indoor Least Crowded Path Query (LCPQ) finds a path
encountering the least objects en route. To process the queries, we design a
unified framework with three major components. First, an indoor crowd model
organizes indoor topology and captures object flows between rooms. Second, a
time-evolving population estimator derives room populations for a future
timestamp to support crowd-aware routing cost computations in query processing.
Third, two exact and two approximate query processing algorithms process each
type of query. All algorithms are based on graph traversal over the indoor
crowd model and use the same search framework with different strategies of
updating the populations during the search process. All proposals are evaluated
experimentally on synthetic and real data. The experimental results demonstrate
the efficiency and scalability of our framework and query processing
algorithms.Comment: The extension of a VLDB'21 paper "Towards Crowd-aware Indoor Path
Planning
Contact Tracing over Uncertain Indoor Positioning Data (Extended Version)
Pandemics often cause dramatic losses of human lives and impact our societies
in many aspects such as public health, tourism, and economy. To contain the
spread of an epidemic like COVID-19, efficient and effective contact tracing is
important, especially in indoor venues where the risk of infection is higher.
In this work, we formulate and study a novel query called Indoor Contact Query
(ICQ) over raw, uncertain indoor positioning data that digitalizes people's
movements indoors. Given a query object o, e.g., a person confirmed to be a
virus carrier, an ICQ analyzes uncertain indoor positioning data to find
objects that most likely had close contact with o for a long period of time. To
process ICQ, we propose a set of techniques. First, we design an enhanced
indoor graph model to organize different types of data necessary for ICQ.
Second, for indoor moving objects, we devise methods to determine uncertain
regions and to derive positioning samples missing in the raw data. Third, we
propose a query processing framework with a close contact determination method,
a search algorithm, and the acceleration strategies. We conduct extensive
experiments on synthetic and real datasets to evaluate our proposals. The
results demonstrate the efficiency and effectiveness of our proposals.Comment: Accepted by TKDE (April.2023
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