Efficient point-based trajectory search

LNCS v. 9239 entitled: Advances in Spatial and Temporal Databases: 14th International Symposium, SSTD 2015, Hong Kong, China, August 26-28, 2015. ProceedingsTrajectory data capture the traveling history of moving objects such as people or vehicles. With the proliferation of GPS and tracking technology, huge volumes of trajectories are rapidly generated and collected. Under this, applications such as route recommendation and traveling behavior mining call for efficient trajectory retrieval. In this paper, we first focus on distance-based trajectory search; given a collection of trajectories and a set query points, the goal is to retrieve the top-k trajectories that pass as close as possible to all query points. We advance the state-of-the-art by combining existing approaches to a hybrid method and also proposing an alternative, more efficient rangebased approach. Second, we propose and study the practical variant of bounded distance-based search, which takes into account the temporal characteristics of the searched trajectories. Through an extensive experimental analysis with real trajectory data, we show that our rangebased approach outperforms previous methods by at least one order of magnitude. © Springer International Publishing Switzerland 2015.postprin

Evidence for Exotic J^{PC}=1^{-+} Meson Production in the Reaction pi- p --> eta pi- p at 18 GeV/c

Details of the analysis of the eta pi- system studied in the reaction pi^{-} p --> eta pi^{-} p at 18 GeV/c are given. Separate analyses for the 2 gamma and pi+ pi- pi0 decay modes of the eta are presented. An amplitude analysis of the data indicates the presence of interference between the a(2)(1320)- and a J^{PC}=1^{-+} wave between 1.2 and 1.6 GeV/c^2. The phase difference between these waves shows phase motion not attributable solely to the a(2)(1320)-. The data can be fitted by interference between the a(2)(1320)- and an exotic 1^{-+} resonance with M = 1370 +-16 +50 -30} MeV/c^2 and Gamma = 385 +- 40 +65 -105 MeV/c^2. Our results are compared with those of other experiments.Comment: 50 pages of text and 34 figure

Evidence for Exotic Meson Production in the Reaction π−p→ηπ−p \pi^{-} p \to \eta \pi^{-} p at 18 GeV/c

The $\eta \pi^{-}$ system has been studied in the reaction $\pi^{-} p \to \eta \pi^{-} p$ at 18 GeV/c. A large asymmetry in the angular distribution is observed indicating interference between L-even and L-odd partial waves. The $a_{2}(1320)$ is observed in the $J^{PC}$ = $2^{++}$ wave, as is a broad enhancement between 1.2 and 1.6 GeV/c^{2} in the $J^{PC} = 1^{-+}$ wave. The observed phase difference between these waves shows that there is phase motion in addition to that due to $a_{2}(1320)$ decay. The data can be fitted by interference between the $a_{2}(1320)$ and an exotic $1^{-+}$ resonance with $M = (1370 \pm 16 {+50}\atop{-30}$) MeV/c^2 and $\Gamma =(385 \pm 40 {+65}\atop{-105}$) MeV/c^2

Extraordinary optical transmission by interference of diffracted wavelets

The phenomenon of extraordinary optical transmission is drawing much attention of researchers because of its potential applications in diverse emerging areas. In the present work, experimental observations on diffraction-Lloyd-mirror interferometer are reported, where two diffracted wavefronts are superimposed using Lloyd’s mirror. These observations provide direct experimental evidence in support of the idea that one of the main reasons of enhanced transmission through subwavelength apertures is the coherent superposition of diffracted wavelets originating from diffractive scattering at the aperture

Exploiting duality in summarization with deterministic guarantees

Summarization is an important task in data mining. A major challenge over the past years has been the efficient construction of fixed-space synopses that provide a deterministic quality guarantee, often expressed in terms of a maximum-error metric. Histograms and several hierarchical techniques have been proposed for this problem. However, their time and/or space complexities remain impractically high and depend not only on the data set size n, but also on the space budget B. These handicaps stem from a requirement to tabulate all allocations of synopsis space to different regions of the data. In this paper we develop an alternative methodology that dispels these deficiencies, thanks to a fruitful application of the solution to the dual problem: given a maximum allowed error, determine the minimum-space synopsis that achieves it. Compared to the state-of-the-art, our histogram construction algorithm reduces time complexity by (at least) a Blog2n over log*factor and our hierarchical synopsis algorithm reduces the complexity by (at least) a factor of log2B over log*+ logn in time and B(1-log B over log n) in space, where *is the optimal error. These complexity advantages offer both a space-efficiency and a scalability that previous approaches lacked. We verify the benefits of our approach in practice by experimentation. © 2007 ACM.link_to_subscribed_fulltex

On enhancing scalability for distributed RDF/S stores

This work presents MIDAS-RDF, a distributed P2P RDF/S repository that is built on top of a distributed multi-dimensional index structure. MIDAS-RDF features fast retrieval of RDF triples satisfying various pattern queries by translating them into multi-dimensional range queries, which can be processed by the underlying index in hops logarithmic to the number of peers. More importantly, MIDAS-RDF utilizes a labeling scheme to handle expensive transitive closure computations efficiently. This allows for distributed RDFS reasoning in a more scalable way compared to existing methods, as also demonstrated by our extensive experimental study. Furthermore, MIDAS-RDF supports a publish-subscribe model that enables remote peers to selectively subscribe to RDF content

Hierarchically compressed wavelet synopses

The wavelet decomposition is a proven tool for constructing concise synopses of large data sets that can be used to obtain fast approximate answers. Existing research studies focus on selecting an optimal set of wavelet coefficients to store so as to minimize some error metric, without however seeking to reduce the size of the wavelet coefficients themselve

SHIFT-SPLIT: I/O efficient maintenance of wavelet-transformed multidimensional data

The Discrete Wavelet Transform is a proven tool for a wide range of database applications. However, despite broad ac-ceptance, some of its properties have not been fully explored and thus not exploited, particularly for two common forms of multidimensional decomposition. We introduce two novel operations for wavelet transformed data, termed SHIFT and SPLIT, based on the properties of wavelet trees, which work directly in the wavelet domain. We demonstrate their sig-nificance and usefulness by analytically proving six impor-tant results in four common data maintenance scenarios, i.e., transformation of massive datasets, appending data, ap-proximation of data streams and partial data reconstruction, leading to significant I/O cost reduction in all cases. Fur-thermore, we show how these operations can be further im-proved in combination with the optimal coefficient-to-disk-block allocation strategy. Our exhaustive set of empirical experiments with real-world datasets verifies our claims. 1

RIPPLE: A Scalable Framework for Distributed Processing of Rank Queries

We introduce a generic framework, termed RIPPLE, for processing rank queries in decentralized systems. Rank queries are particularly challenging, since the search area (i.e., which tuples qualify) cannot be determined by any peer individually. While our proposed framework is generic enough to apply to all decentralized structured systems, we show that when coupled with a particular distributed hash table (DHT) topology, it offers guaranteed worst-case performance. Specifically, rank query processing in our framework exhibits tunable polylogarithmic latency, in terms of the network size. Additionally we provide a means to trade-off latency for communication and processing cost. As a proof of concept, we apply RIPPLE for top-k query processing. Then, we consider skyline queries, and demonstrate that our framework results in a method that has better latency and lower overall communication cost than existing approaches over DHTs. Finally, we provide a RIPPLEbased approach for constructing a k-diversified set, which, to the best of our knowledge, is the first distributed solution for this problem. Extensive experiments with real and synthetic datasets validate the effectiveness of our framework