Lamps: Location-Aware Moving Top-k Pub/Sub (Extended abstract)

We propose a novel system, called Lamps (Location-Aware Moving Top-k Pub/Sub), which continuously monitors the top-k most relevant spatio-textual objects for a large number of moving top-k spatio-textual subscriptions simultaneously. Lamps employs the concept of a safe region to monitor top-k results. However, unlike with existing works that assume static objects, top-k result updates may be triggered by newly generated objects. To continuously monitor the top-k results for massive moving subscriptions efficiently, we propose SQ-tree, a novel index based on safe regions, to filter subscriptions whose top-k results do not change. Moreover, to reduce the expensive cost of safe region re-evaluation, we develop a novel approximation technique for safe region construction. Our experimental results on real datasets show that Lamps achieves higher performance than baseline approaches.Nishio S., Amagata D., Hara T.. Lamps: Location-Aware Moving Top-k Pub/Sub (Extended abstract). Proceedings - International Conference on Data Engineering 2023-April, 3809 (2023); https://doi.org/10.1109/ICDE55515.2023.00331

Approximate Reverse Top-k Spatial-Keyword Queries

Location-based services are becoming more involved with our daily lives, so many works have considered efficiently retrieving useful objects from spatial-keyword databases. These works are promising on the user sides, but none of them considers the service provider sides. To gain profits and enrich recommendation lists, service providers conduct market analyses and want to know potential users who may be interested in their services. In this paper, to satisfy this requirement, we propose a new query, approximate reverse top-k spatial-keyword (ART) query. Given a set O of spatial-keyword objects, a set S of users (their locations and preferable keywords), a query object q, k, and an approximation ratio ϵ, an ART query retrieves such users that q is included in their approximate top-k results among O and q. A straightforward approach to processing this query is to run a top-k spatial-keyword search for each user in S. This is clearly expensive, as the number of users is generally large. We therefore propose PART, an efficient algorithm for ART query processing. In addition, we propose B-PART, which enables the processing of multiple ART queries in a batch. We conduct extensive experiments using real datasets, and the results demonstrate the efficiencies of our algorithms.Nishio S., Amagata D., Hara T.. Approximate Reverse Top-k Spatial-Keyword Queries. Proceedings - IEEE International Conference on Mobile Data Management 2023-July, 96 (2023); https://doi.org/10.1109/MDM58254.2023.00026

Discord Monitoring for Streaming Time-Series

Many applications generate time-series and analyze it. One of the most important time-series analysis tools is anomaly detection, and discord discovery aims at finding an anomaly subsequence in a time-series. Time-series is essentially dynamic, so monitoring the discord of a streaming time-series is an important problem. This paper addresses this problem and proposes SDM (Streaming Discord Monitoring), an algorithm that efficiently updates the discord of a streaming time-series over a sliding window. We show that SDM is approximation-friendly, i.e., the computational efficiency is accelerated by monitoring an approximate discord with theoretical bound. Our experiments on real datasets demonstrate the efficiency of SDM and its approximate version.This version of the contribution has been accepted for publication, after peer review (when applicable) but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/978-3-030-27615-7_6. Use of this Accepted Version is subject to the publisher’s Accepted Manuscript terms of use https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms.Kato S., Amagata D., Nishio S., et al. Discord Monitoring for Streaming Time-Series. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 11706 LNCS, 79 (2019

Distributed Spatial-Keyword kNN Monitoring for Location-aware Pub/Sub

Recent applications employ publish/subscribe (Pub/Sub) systems so that publishers can easily receive attentions of customers and subscribers can monitor useful information generated by publishers. Due to the prevalence of smart devices and social networking services, a large number of objects that contain both spatial and keyword information have been generated continuously, and the number of subscribers also continues to increase. This poses a challenge to Pub/Sub systems: they need to continuously extract useful information from massive objects for each subscriber in real time. In this paper, we address the problem of k nearest neighbor monitoring on a spatial-keyword data stream for a large number of subscriptions. To scale well to massive objects and subscriptions, we propose a distributed solution. Given m workers, we divide a set of subscriptions into m disjoint subsets based on a cost model so that each worker has almost the same kNN-update cost, to maintain load balancing. We allow an arbitrary approach to updating kNN of each subscription, so with a suitable in-memory index, our solution can accelerate update efficiency by pruning irrelevant subscriptions for a given new object. We conduct experiments on real datasets, and the results demonstrate the efficiency and scalability of our solution

Autonomous mobile robot for outdoor slope using 2D LiDAR with uniaxial gimbal mechanism

The Nakanoshima Challenge is a contest for developing sophisticated navigation systems of robots for collecting garbage in outdoor public spaces. In this study, a robot named Navit(oo)n is designed, and its performance in public spaces such as city parks is evaluated. Navit(oo)n contains two 2D LiDAR scanners with uniaxial gimbal mechanism, improving self-localization robustness on a slope. The gimbal mechanism adjusts the angle of the LiDAR scanner, preventing erroneous ground detection. We evaluate the navigation performance of Navit(oo)n in the Nakanoshima and its Extra Challenges

Approximate Reverse Top-k Spatial-Keyword Queries

Nishio S., Amagata D., Hara T.. Approximate Reverse Top-k Spatial-Keyword Queries. Proceedings - IEEE International Conference on Mobile Data Management 2023-July, 96 (2023); https://doi.org/10.1109/MDM58254.2023.00026.Location-based services are becoming more involved with our daily lives, so many works have considered efficiently retrieving useful objects from spatial-keyword databases. These works are promising on the user sides, but none of them considers the service provider sides. To gain profits and enrich recommendation lists, service providers conduct market analyses and want to know potential users who may be interested in their services. In this paper, to satisfy this requirement, we propose a new query, approximate reverse top-k spatial-keyword (ART) query. Given a set O of spatial-keyword objects, a set S of users (their locations and preferable keywords), a query object q, k, and an approximation ratio ϵ, an ART query retrieves such users that q is included in their approximate top-k results among O and q. A straightforward approach to processing this query is to run a top-k spatial-keyword search for each user in S. This is clearly expensive, as the number of users is generally large. We therefore propose PART, an efficient algorithm for ART query processing. In addition, we propose B-PART, which enables the processing of multiple ART queries in a batch. We conduct extensive experiments using real datasets, and the results demonstrate the efficiencies of our algorithms

Lamps: Location-Aware Moving Top-k Pub/Sub (Extended abstract)

Nishio S., Amagata D., Hara T.. Lamps: Location-Aware Moving Top-k Pub/Sub (Extended abstract). Proceedings - International Conference on Data Engineering 2023-April, 3809 (2023); https://doi.org/10.1109/ICDE55515.2023.00331.We propose a novel system, called Lamps (Location-Aware Moving Top-k Pub/Sub), which continuously monitors the top-k most relevant spatio-textual objects for a large number of moving top-k spatio-textual subscriptions simultaneously. Lamps employs the concept of a safe region to monitor top-k results. However, unlike with existing works that assume static objects, top-k result updates may be triggered by newly generated objects. To continuously monitor the top-k results for massive moving subscriptions efficiently, we propose SQ-tree, a novel index based on safe regions, to filter subscriptions whose top-k results do not change. Moreover, to reduce the expensive cost of safe region re-evaluation, we develop a novel approximation technique for safe region construction. Our experimental results on real datasets show that Lamps achieves higher performance than baseline approaches

Discord Monitoring for Streaming Time-Series

This version of the contribution has been accepted for publication, after peer review (when applicable) but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/978-3-030-27615-7_6. Use of this Accepted Version is subject to the publisher’s Accepted Manuscript terms of use https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms.Kato S., Amagata D., Nishio S., et al. Discord Monitoring for Streaming Time-Series. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 11706 LNCS, 79 (2019)Many applications generate time-series and analyze it. One of the most important time-series analysis tools is anomaly detection, and discord discovery aims at finding an anomaly subsequence in a time-series. Time-series is essentially dynamic, so monitoring the discord of a streaming time-series is an important problem. This paper addresses this problem and proposes SDM (Streaming Discord Monitoring), an algorithm that efficiently updates the discord of a streaming time-series over a sliding window. We show that SDM is approximation-friendly, i.e., the computational efficiency is accelerated by monitoring an approximate discord with theoretical bound. Our experiments on real datasets demonstrate the efficiency of SDM and its approximate version

Distributed Spatial-Keyword kNN Monitoring for Location-aware Pub/Sub

Recent applications employ publish/subscribe (Pub/Sub) systems so that publishers can easily receive attentions of customers and subscribers can monitor useful information generated by publishers. Due to the prevalence of smart devices and social networking services, a large number of objects that contain both spatial and keyword information have been generated continuously, and the number of subscribers also continues to increase. This poses a challenge to Pub/Sub systems: they need to continuously extract useful information from massive objects for each subscriber in real time. In this paper, we address the problem of k nearest neighbor monitoring on a spatial-keyword data stream for a large number of subscriptions. To scale well to massive objects and subscriptions, we propose a distributed solution. Given m workers, we divide a set of subscriptions into m disjoint subsets based on a cost model so that each worker has almost the same kNN-update cost, to maintain load balancing. We allow an arbitrary approach to updating kNN of each subscription, so with a suitable in-memory index, our solution can accelerate update efficiency by pruning irrelevant subscriptions for a given new object. We conduct experiments on real datasets, and the results demonstrate the efficiency and scalability of our solution