Investigating Impact of Quorum Construction on Data Processing in Mobile Ad Hoc Networks

In a mobile ad hoc network (MANET), since mobility of mobile hosts causes frequent network partitioning, consistency management of data operations on replicas becomes a crucial issue. in our previous work, we have defined several consistency levels for MANET applications and designed protocols to achieve these consistency levels. These protocols are mainly based on a dynamic quorum system to cope with network partitioning and node and network failures. in this paper, we further investigate the impact of quorum construction on the system performance through simulation studies. Specifically, we change the number of mobile hosts that replicate data items, and which hosts replicate each data item in the simulations and examine the impact on the system performance in terms of data availability and traffic. © 2010 IEEE

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

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

Thermally Assisted Penetration and Exclusion of Single Vortex in Mesoscopic Superconductors

A single vortex overcoming the surface barrier in a mesoscopic superconductor with lateral dimensions of several coherence lengths and thickness of several nanometers provides an ideal platform to study thermal activation of a single vortex. In the presence of thermal fluctuations, there is non-zero probability for vortex penetration into or exclusion from the superconductor even when the surface barrier does not vanish. We consider the thermal activation of a single vortex in a mesoscopic superconducting disk of circular shape. To obtain statistics for the penetration and exclusion magnetic fields, slow and periodic magnetic fields are applied to the superconductor. We calculate the distribution of the penetration and exclusion fields from the thermal activation rate. This distribution can also be measured experimentally, which allows for a quantitative comparison.Comment: 7 pages, 4 figure

ESTIMATION OF TIDAL ENERGY DISSIPATION AND DIAPYCNAL DIFFUSIVITY IN THE INDONESIAN SEAS

The Indonesian Seas separating the Indian Ocean from the West Pacific Oceanare representative regions of strong tidal mixing in the world oceans. In the present study,we first carry out numerical simulation of the barotropic tidal elevation field in theIndonesian Seas using horizontally two-dimensional primitive equation model. It is foundthat, to reproduce realistic tidal elevations in the Indonesian Seas, the energy lost by theincoming barotropic tides to internal waves within the Indonesian seas should be taken intoaccount. The numerical experiments show that the model predicted tidal elevations in theIndonesian Seas best fit the observed data when we take into account the baroclinic energyconversion in the Indonesian Seas ~86.1 GW for the M2 tidal constituent and ~134.6 GWfor the major four tidal constituents (M2, S2, K1, O1). For this baroclinic energy conversion,the value of Kñ averaged within the eastern area (Halmahera, Seram, Banda and MalukuSeas), the western area (Makassar and Flores Seas), and the southern area (Lombok Straitand Timor Passage) are estimated to be ~23 × 10-4 m2s-1, ~5 × 10-4 m2s-1, and ~10× 10-4m2s-1, respectively. This value is about 1 order of magnitude more than assumed for theIndonesian Seas in previous ocean general circulation models. We offer this study as awarning against using diapycnal diffusivity just as a tuning parameter to reproduce largescalephenomena

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