Sidewalk-level positions are required for a growing number of pedestrian applications. However, in urban canyons, buildings along both sides of the street severely obstruct global navigation satellite system (GNSS) signals, and the lack of redundant fault-free measurements leads to the poor accuracy in the cross-street direction, posing challenges in determining the side of the street solely based on GNSS positions. While 3-D building models have been utilized to improve position accuracy, particularly in the cross-street direction, techniques relying on these models face issues, such as position ambiguity, high computational load, and low accuracy in the along-street direction. In this study, we aim to develop a novel intelligent urban positioning system using smartphone sensors and pedestrian network. An algorithm is proposed to determine the side of the street by analyzing which half of the sky most of the line-of-sight (LOS) signals are observed. The additional virtual measurement derived from the sidewalk is combined with real measurements to solve GNSS position. It can achieve sidewalk-level positioning since the redundancy in the cross-street direction is significantly improved. The proposed system offers several advantages including elimination of the need for LOS/NLOS signal identification for each satellite and elimination of the need for 3-D building models. Extensive data sets were utilized to train the classification model and evaluate the system’s performance. The results demonstrate a correct identification rate of better than 96% using single epoch GNSS observations. More importantly, the proposed positioning system achieves the accuracy of better than 5 m in urban canyons.

The Global Navigation Satellite System (GNSS) has gained widespread use in smartphones, providing support for various pedestrian applications. In urban areas, multipath effects introduce large errors in different measurements, severely degrading the GNSS accuracy. To mitigate multipath effects, several Fault Detection and Exclusion (FDE) methods have been developed. However, in urban canyons, their effectiveness is significantly degraded due to the lack of fault-free measurements in the cross-street direction. In urban canyons, the pedestrian network provides an opportunity to improve the urban GNSS accuracy for pedestrians. The purpose of this study is to improve the GNSS FDE performance through the sidewalk constraints. A new scheme has been proposed to distinguish the correct side of the street effectively. The Hough Transform estimator was used to find the most consistent GNSS measurements under sidewalk constraints. To assess the proposed algorithm’s performance, extensive tests were conducted in urban canyons. The analysis of Carrier-to-Noise density ratio (C/N0) shows that 92% of sidewalks can be distinguished from the opposite sidewalk along the same street. The static test shows that the positioning accuracy can be improved from 22 m to 4.9 m, a 77% improvement over the residual based FDE. The dynamic test showed that the proposed method can achieve the sidewalk positioning, which is essential for many pedestrian applications such as last-mile delivery, emergency caller positioning and jaywalking monitoring.

TASTIER is a research project on the new information-access paradigm called type-ahead search, in which systems find answers to a keyword query on-the-fly as users type in the query. In this paper we study how to support fuzzy type-ahead search in TASTIER. Supporting fuzzy search is important when users have limited knowledge about the exact representation of the entities they are looking for, such as people records in an online directory. We have developed and deployed several such systems, some of which have been used by many people on a daily basis. The systems received overwhelmingly positive feedbacks from users due to their friendly interfaces with the fuzzy-search feature. We describe the design and implementation of the systems, and demonstrate several such systems. We show that our efficient techniques can indeed allow this search paradigm to scale on large amounts of data.

MOTIVATION: The MEDLINE database, consisting of over 19 million publication records, is the primary source of information for biomedicine and health questions. Although the database itself has been growing rapidly, the search paradigm of MEDLINE has remained largely unchanged. RESULTS: Here, we propose a new system for exploring the entire MEDLINE collection, represented by two unique features: (i) interactive: providing instant feedback to users' query letter by letter, and (ii) fuzzy: allowing approximate search. We develop novel index structures and search algorithms to make such a search model possible. We also develop incremental-update techniques to keep the data up to date. AVAILABILITY: Interactive and fuzzy searching algorithms for exploring MEDLINE are implemented in a system called iPubMed, freely accessible over the web at http://ipubmed.ics.uci.edu/ and http://tastier.cs.tsinghua.edu.cn/ipubmed.

Answering approximate queries on string collections is important in applications such as data cleaning, query relaxation, and spell checking, where inconsistencies and errors exist in user queries as well as data. Many existing algorithms use gram-based inverted-list indexing structures to answer approximate string queries. These indexing structures are "notoriously" large compared to the size of their original string collection. In this paper, we study how to reduce the size of such an indexing structure to a given amount of space, while retaining efficient query processing. We first study how to adopt existing inverted-list compression techniques to solve our problem. Then, we propose two novel approaches for achieving the goal: one is based on discarding gram lists, and one is based on combining correlated lists. They are both orthogonal to existing compression techniques, exploit a unique property of our setting, and offer new opportunities for improving query performance. For each approach we analyze its effect on query performance and develop algorithms for wisely choosing lists to discard or combine. Our extensive experiments on real data sets show that our approaches provide applications the flexibility in deciding the tradeoff between query performance and indexing size, and can outperform existing compression techniques. An interesting and surprising finding is that while we can reduce the index size significantly (up to 60% reduction) with tolerable performance penalties, for 20-40% reductions we can even improve query performance compared to original indexes.

Existing keyword-search systems in relational databases require users to submit a complete query to compute answers. Often users feel "left in the dark" when they have limited knowledge about the data, and have to use a try-and-see approach for modifying queries and finding answers. In this paper we propose a novel approach to keyword search in the relational world, called Tastier. A Tastier system can bring instant gratification to users by supporting type-ahead search, which finds answers "on the fly" as the user types in query keywords. A main challenge is how to achieve a high interactive speed for large amounts of data in multiple tables, so that a query can be answered efficiently within milliseconds. We propose efficient index structures and algorithms for finding relevant answers on-the-fly by joining tuples in the database. We devise a partition-based method to improve query performance by grouping highly relevant tuples and pruning irrelevant tuples efficiently. We also develop a technique to answer a query efficiently by predicting the highly relevant complete queries for the user. We have conducted a thorough experimental evaluation of the proposed techniques on real data sets to demonstrate the efficiency and practicality of this new search paradigm.

Traditional information systems return answers after a user submits a complete query. Users often feel "left in the dark" when they have limited knowledge about the underlying data, and have to use a try-and-see approach for finding information. A recent trend of supporting autocomplete in these systems is a first step towards solving this problem. In this paper, we study a new information-access paradigm, called "interactive, fuzzy search," in which the system searches the underlying data "on the fly" as the user types in query keywords. It extends autocomplete interfaces by (1) allowing keywords to appear in multiple attributes (in an arbitrary order) of the underlying data; and (2) finding relevant records that have keywords matching query keywords approximately. This framework allows users to explore data as they type, even in the presence of minor errors. We study research challenges in this framework for large amounts of data. Since each keystroke of the user could invoke a query on the backend, we need efficient algorithms to process each query within milliseconds. We develop various incremental-search algorithms using previously computed and cached results in order to achieve an interactive speed. We have deployed several real prototypes using these techniques. One of them has been deployed to support interactive search on the UC Irvine people directory, which has been used regularly and well received by users due to its friendly interface and high efficiency.

Surveillance is an important task for guaranteeing the security of individuals. Being able to intelligently monitor the activity in given spaces is essential to achieve such surveillance. Sentient spaces based on a large set of sensors provide the potential for such intelligent monitoring. However, heavily instrumenting a space with sensors it is not enough to build a sentient space. One needs a software architecture that allows programming all these sensors in a transparent and efficient manner. In this paper, we present SATware, a stream acquisition and transformation middleware we are developing to analyze, query, and transform multimodal sensor data streams to facilitate flexible development of sentient environments. SATware provides a powerful application development environment in which users (i.e., application builders) can focus on the specifics of the application without having to deal with the technical peculiarities of accessing a large number of diverse sensors via different protocols.