Showing papers on "Skyline published in 2016"
TL;DR: An adaptive algorithm, called ADSUD, is proposed, which redefine the approximate global skyline probability and choose local representative tuples due to minimum probabilistic bounding rectangle adaptively and design a progressive pruning method and apply the reuse mechanism to improve its efficiency.
Abstract: Query processing over uncertain data has gained growing attention, because it is necessary to deal with uncertain data in many real-life applications. In this paper, we investigate skyline queries over uncertain data in distributed environments (DSUD query) whose research is only in an early stage. The state-of-the-art algorithm, called e-DSUD algorithm, is designed for processing this query. It has the desirable characteristics of progressiveness and minimum bandwidth consumption. However, it still needs to be perfected in three aspects. (1) Progressiveness. Each time it only returns one query result at most. (2) Efficiency. There are a significant amount of redundant I/O cost and numerous iterations which causes a long total query time. (3) Universality. It is restricted to the case where local skyline tuples are incomparability. To address these concerns, we first present a detailed analysis of the e-DSUD algorithm and then develop an improved framework for the DSUD query, namely IDSUD. Based on the new framework, we propose an adaptive algorithm, called ADSUD, for the DSUD query. In the algorithm, we redefine the approximate global skyline probability and choose local representative tuples due to minimum probabilistic bounding rectangle adaptively. Furthermore, we design a progressive pruning method and apply the reuse mechanism to improve its efficiency. The results of extensive experiments verify the better overall performance of our algorithm than the e-DSUD algorithm.
82 citations
TL;DR: This paper forms an uncertain dynamic skyline (UDS) query over a probabilistic product set, and proposes effective pruning strategies for the UDS query, and integrates them into effective algorithms.
Abstract: With the development of the economy, products are significantly enriched, and uncertainty has been their inherent quality. The probabilistic dynamic skyline (PDS) query is a powerful tool for customers to use in selecting products according to their preferences. However, this query suffers several limitations: it requires the specification of a probabilistic threshold, which reports undesirable results and disregards important results; it only focuses on the objects that have large dynamic skyline probabilities; and, additionally, the results are not stable. To address this concern, in this paper, we formulate an uncertain dynamic skyline (UDS) query over a probabilistic product set. Furthermore, we propose effective pruning strategies for the UDS query, and integrate them into effective algorithms. In addition, a novel query type, namely the top $k$ favorite probabilistic products (TFPP) query, is presented. The TFPP query is utilized to select $k$ products which can meet the needs of a customer set at the maximum level. To tackle the TFPP query, we propose a TFPP algorithm and its efficient parallelization. Extensive experiments with a variety of experimental settings illustrate the efficiency and effectiveness of our proposed algorithms.
49 citations
TL;DR: Recognizing the lack of studies that incorporate the MapReduce framework into parallel skyline processing, this research proposes a partialpresort grid-based partition skyline algorithm that is able to significantly improve the merging skyline computation on large datasets.
Abstract: This research develops an advanced two-phase MapReduce solution that is able to efficiently address skyline queries on large datasets. Unlike existing parallel skyline approaches, our scheme considers data partitioning, filtering, and parallel skyline evaluation as a holistic query process. In particular, we apply filtering techniques and angle-based partitioning in the first phase, in which unqualified objects are discarded and the processed objects are partitioned by their angles to the origin.In the second phase, local skyline objects in each partition are calculated in parallel, and global skyline objects are output after a merging skyline process. To improve the parallel local skyline calculation, we propose two partition-aware filtering methods that keep skyline candidates in a balanced manner. The aggressive partition-aware filtering aggressively eliminates objects in the partition with the greatest population of candidate objects, whereas the proportional partition-aware filtering slows down the growth of partition population proportionally.Recognizing the lack of studies that incorporate the MapReduce framework into parallel skyline processing, we propose a partial-presort grid-based partition skyline algorithm that is able to significantly improve the merging skyline computation on large datasets. The presort process can be completed in the shuffle phase with little overhead. Our experimental results show the efficiency and effectiveness of the proposed parallel skyline solution utilizing MapReduce on large-scale datasets.
46 citations
TL;DR: This work proposes an integrated skyline query processing method that is shortened by three times, when compared with two state-of-the-art methods, to discover qualified services and compose them with guaranteed quality of service (QoS) over multiple clouds.
Abstract: A cloud mashup is composed of multiple services with shared datasets and integrated functionalities. For example, the elastic compute cloud (EC2) provided by Amazon Web Service (AWS), the authentication and authorization services provided by Facebook, and the Map service provided by Google can all be mashed up to deliver real-time, personalized driving route recommendation service. To discover qualified services and compose them with guaranteed quality of service (QoS), we propose an integrated skyline query processing method for building up cloud mashup applications. We use a similarity test to achieve optimal localized skyline. This mashup method scales well with the growing number of cloud sites involved in the mashup applications. Faster skyline selection, reduced composition time, dataset sharing, and resources integration assure the QoS over multiple clouds. We experiment with the quality of web service (QWS) benchmark over 10,000 web services along six QoS dimensions. By utilizing block-elimination, data-space partitioning, and service similarity pruning, the skyline process is shortened by three times, when compared with two state-of-the-art methods.
43 citations
TL;DR: This work presents a systematic workflow for the straightforward method design and analysis of selected reaction monitoring data in lipidomics based on lipid building blocks using Skyline, a powerful software primarily designed for proteomics applications where it is widely used.
Abstract: In response to the urgent need for analysis software that is capable of handling data from targeted high-throughput lipidomics experiments, we here present a systematic workflow for the straightforward method design and analysis of selected reaction monitoring data in lipidomics based on lipid building blocks. Skyline is a powerful software primarily designed for proteomics applications where it is widely used. We adapted this tool to a “Plug and Play” system for lipid research. This extension offers the unique capability to assemble targeted mass spectrometry methods for complex lipids easily by making use of building blocks. With simple yet tailored modifications, targeted methods to analyze main lipid classes such as glycerophospholipids, sphingolipids, glycerolipids, cholesteryl-esters, and cholesterol can be quickly introduced into Skyline for easy application by end users without distinct bioinformatics skills. To illustrate the benefits of our novel strategy, we used Skyline to quantify sphingolipi...
39 citations
TL;DR: Detailed security analysis shows that the proposed EPSC framework can achieve multi-domain skyline computation without leaking sensitive information to each other and performance evaluations via extensive simulations demonstrate the EPSC's efficiency in terms of providing skyline computation and transmission while minimizing the privacy disclosure across different domains.
37 citations
10 Apr 2016
TL;DR: Three novel schemes are proposed that enable efficient verification of any LBSQ result returned by an untrusted CSP by embedding and exploring a novel neighboring relationship among POIs.
Abstract: Recent years have witnessed a growing number of location-based service providers (LBSPs) outsourcing their points of interest (POI) datasets to third-party cloud service providers (CSPs), which in turn answer various data queries from mobile users on their behalf. A main challenge in such systems is that the CSPs cannot be fully trusted, which may return fake query results for various bad motives, e.g., in favor of POIs willing to pay. As an important type of queries, location-based skyline queries (LBSQs) ask for the POIs that are not spatially dominated by any other POI with respect to some query position. In this paper, we propose three novel schemes that enable efficient verification of any LBSQ result returned by an untrusted CSP by embedding and exploring a novel neighboring relationship among POIs. The efficacy and efficiency of our schemes are thoroughly analyzed and evaluated.
33 citations
27 Jun 2016
TL;DR: This paper investigates a methodology for web service selection that combines three techniques, using skyline to reduce the research space, BWM (Best Worst Method) to assign weight to QoS criteria and finally Vikor to rank the obtained skyline web services.
Abstract: The fast growth of the number of deployed Web services is transforming the enterprises from data-oriented systems into service-oriented systems. In this context, the selection of the best web service that satisfies specific QoS values among a group of web services with similar functionalities is becoming a challenging issue. To this aim, we investigate in this paper a methodology for web service selection that combines three techniques. First of all, we use skyline to reduce the research space, then BWM (Best Worst Method) to assign weight to QoS criteria and finally we exploit Vikor to rank the obtained skyline web services. We report the experimental results that compare our approach with Topsis and Promethee methods.
31 citations
TL;DR: A new sorting-based bucket skyline algorithm is proposed using two optimization techniques: bucket- and point-level orders and a novel skyline ranking method that adjusts two user-specific parameters for retrieving meaningful skyline points.
29 citations
TL;DR: The experimental results show that the proposed approach for processing skyline queries in incomplete database has significantly reduced the number of pairwise comparisons and the processing time in determining the skylines compared to the previous approaches.
Abstract: In recent years, there has been great attention given to skyline queries that incorporate and provide more flexible query operators that return data items (skylines) which are not being dominated by other data items in all dimensions (attributes) of the database. Many variations in skyline techniques have been proposed in the literature. However, most of these techniques determine skylines by assuming that the values of all dimensions for every data item are available (complete). But this assumption is not always true particularly for large multidimensional database as some values may be missing (not applicable during the computation). In this paper, we proposed an efficient approach for processing skyline queries in incomplete database. The experimental results show that our proposed approach has significantly reduced the number of pairwise comparisons and the processing time in determining the skylines compared to the previous approaches.
27 citations
14 Jun 2016
TL;DR: This work proposes a sharing-aware multi- query execution strategy for outlier detection on data streams called SOP, and designs a customized skyline algorithm called K-SKY that leverages the domination relationships among the streaming data points to minimize the number of data points that must be evaluated for supporting multi-query outlier Detection.
Abstract: Real-time analytics of anomalous phenomena on streaming data typically relies on processing a large variety of continuous outlier detection requests, each configured with different parameter settings. The processing of such complex outlier analytics workloads is resource consuming due to the algorithmic complexity of the outlier mining process. In this work we propose a sharing-aware multi-query execution strategy for outlier detection on data streams called SOP. A key insight of SOP is to transform the problem of handling a multi-query outlier analytics workload into a single-query skyline computation problem. We prove that the output of the skyline computation process corresponds to the minimal information needed for determining the outlier status of any point in the stream. Based on this new formulation, we design a customized skyline algorithm called K-SKY that leverages the domination relationships among the streaming data points to minimize the number of data points that must be evaluated for supporting multi-query outlier detection. Based on this K-SKY algorithm, our SOP solution achieves minimal utilization of both computational and memory resources for the processing of these complex outlier analytics workload. Our experimental study demonstrates that SOP consistently outperforms the state-of-art solutions by three orders of magnitude in CPU time, while only consuming 5% of their memory footprint - a clear win-win. Furthermore, SOP is shown to scale to large workloads composed of thousands of parameterized queries.
TL;DR: In this article, the skyline operator is applied to scientist ranking to identify those scientists whose performance cannot be surpassed by others’ with respect to all attributes.
TL;DR: This paper systematically study the problem of k-dominant skyline queries on incomplete data (IkDS), where the data objects might miss their attribute values, and presents three efficient algorithms for finding k-Dominant skyline objects over incomplete data.
01 Mar 2016
TL;DR: The research in the paper shows that the critical factor affecting the cost of skyline discovery is the type of search interface controls provided by the website, and develops efficient algorithms for three most popular types, i.e., one-ended range, free range and point predicates, and then combines them to support web databases that feature a mixture of these types.
Abstract: Many web databases are "hidden" behind proprietary search interfaces that enforce the top-k output constraint, i.e., each query returns at most k of all matching tuples, preferentially selected and returned according to a proprietary ranking function. In this paper, we initiate research into the novel problem of skyline discovery over top-k hidden web databases. Since skyline tuples provide critical insights into the database and include the top-ranked tuple for every possible ranking function following the monotonic order of attribute values, skyline discovery from a hidden web database can enable a wide variety of innovative third-party applications over one or multiple web databases. Our research in the paper shows that the critical factor affecting the cost of skyline discovery is the type of search interface controls provided by the website. As such, we develop efficient algorithms for three most popular types, i.e., one-ended range, free range and point predicates, and then combine them to support web databases that feature a mixture of these types. Rigorous theoretical analysis and extensive real-world online and offline experiments demonstrate the effectiveness of our proposed techniques and their superiority over baseline solutions.
TL;DR: Experimental results on both synthetic and real-world data show that the proposed $\epsilon$ -greedy algorithm can solve the problem of calculating the k -LDS efficiently and with a competitive accuracy.
Abstract: A representative skyline contains $k$ skyline points that can represent its corresponding full skyline. The existing measuring criteria of $k$ representative skylines are specifically designed for static data, and they cannot effectively handle streaming data. In this paper, we focus on the problem of calculating the $k$ representative skyline over data streams. First, we propose a new criterion to choose $k$ skyline points as the $k$ representative skyline for data stream environments, termed the $k$ largest dominance skyline ( $k$ -LDS), which is representative to the entire data set and is highly stable over the streaming data. Second, we propose an efficient exact algorithm, called Prefix-based Algorithm (PBA), to solve the $k$ -LDS problem in a 2-dimensional space. The time complexity of PBA is only $\mathcal {O}((M-k)\times k)$ where $M$ is the size of the full skyline set. Third, the $k$ -LDS problem for a $d$ -dimensional ( $d\ge 3$ ) space turns out to be very complex. Therefore, a greedy algorithm is designed to answer $k$ -LDS queries. To further accelerate the calculation, we propose a $\epsilon$ -greedy algorithm which can achieve an approximate factor of $\frac{1}{(1+\epsilon)}(1-\frac{1}{\sqrt{e}})$ . Experimental results on both synthetic and real-world data show that our $k$ -LDS significantly outperforms its competitors in data stream environments. Furthermore, we demonstrate that the proposed $\epsilon$ -greedy algorithm can solve $k$ -LDS efficiently and with a competitive accuracy.
TL;DR: This work model the skyline queries on uncertain time series, and develops a two-step procedure to answer the probabilistic skyline querieson the dataset, and introduces a solution to improve the efficiency of pruning strategies by sharing the computation for two adjacent intervals.
TL;DR: It is found that for suburban scenes in temperate zones, where the skyline is dominated by trees and artificial objects like houses, a “local” UV segmentation with adaptive thresholds applied to individual images leads to the most reliable classification, and UV-only segmentation achieves a quality comparable to multi-spectral (UV and green) segmentation.
Abstract: Evidence from behavioral experiments suggests that insects use the skyline as a cue for visual navigation. However, changes of lighting conditions, over hours, days or possibly seasons, significantly affect the appearance of the sky and ground objects. One possible solution to this problem is to extract the “skyline” by an illumination-invariant classification of the environment into two classes, ground objects and sky. In a previous study (Insect models of illumination-invariant skyline extraction from UV (ultraviolet) and green channels), we examined the idea of using two different color channels available for many insects (UV and green) to perform this segmentation. We found out that for suburban scenes in temperate zones, where the skyline is dominated by trees and artificial objects like houses, a “local” UV segmentation with adaptive thresholds applied to individual images leads to the most reliable classification. Furthermore, a “global” segmentation with fixed thresholds (trained on an image dataset recorded over several days) using UV-only information is only slightly worse compared to using both the UV and green channel. In this study, we address three issues: First, to enhance the limited range of environments covered by the dataset collected in the previous study, we gathered additional data samples of skylines consisting of minerals (stones, sand, earth) as ground objects. We could show that also for mineral-rich environments, UV-only segmentation achieves a quality comparable to multi-spectral (UV and green) segmentation. Second, we collected a wide variety of ground objects to examine their spectral characteristics under different lighting conditions. On the one hand, we found that the special case of diffusely-illuminated minerals increases the difficulty to reliably separate ground objects from the sky. On the other hand, the spectral characteristics of this collection of ground objects covers well with the data collected in the skyline databases, increasing, due to the increased variety of ground objects, the validity of our findings for novel environments. Third, we collected omnidirectional images, as often used for visual navigation tasks, of skylines using an UV-reflective hyperbolic mirror. We could show that “local” separation techniques can be adapted to the use of panoramic images by splitting the image into segments and finding individual thresholds for each segment. Contrarily, this is not possible for ‘global’ separation techniques.
01 Dec 2016
TL;DR: A new skyline algorithm, SkyAlign, is introduced that is designed for the GPU, and a GPU-friendly, grid-based tree structure upon which the algorithm relies, and the search tree allows the algorithm to achieve orders of magnitude faster performance.
Abstract: The skyline operator determines points in a multidimensional dataset that offer some optimal trade-off. State-of-the-art CPU skyline algorithms exploit quad-tree partitioning with complex branching to minimise the number of point-to-point comparisons. Branch-phobic GPU skyline algorithms rely on compute throughput rather than partitioning, but fail to match the performance of sequential algorithms. In this paper, we introduce a new skyline algorithm, SkyAlign, that is designed for the GPU, and a GPU-friendly, grid-based tree structure upon which the algorithm relies. The search tree allows us to dramatically reduce the amount of work done by the GPU algorithm by avoiding most point-to-point comparisons at the cost of some compute throughput. This trade-off allows SkyAlign to achieve orders of magnitude faster performance than its predecessors. Moreover, a NUMA-oblivious port of SkyAlign outperforms native multicore state of the art on challenging workloads by an increasing margin as more cores and sockets are utilised.
TL;DR: Experimental evaluations of the proposed method for selecting good locations shows that it is able to find reasonable number of desirable skyline areas and can help users to find good locations.
Abstract: We present a method for selecting good locations, each of which is close to desirable facilities such as stations, warehouses, promising customers’ house, etc. and is far from undesirable facilities such as competitors’ shops, noise sources, etc. Skyline query, which selects non-dominated objects, is a well known method for selecting small number of desirable objects. We use the idea of skyline queries to select good locations. However, locations are two dimensional data, while objects in the problem of conventional skyline queries are zero dimensional data. Comparison of two dimensional data is much more complicated than that of zero dimensional data. In this paper, we solve the problem of skyline query for two dimensional data, i.e., areas in a map. Experimental evaluations of the proposed method shows that our approach is able to find reasonable number of desirable skyline areas and can help users to find good locations.
TL;DR: This paper studies the skyline problem of fuzzy preference queries, which is, given a set of geo-textual data, the skyline comprises the objects that are not dominated by others, and introduces two functions to quantify the text relevance and the spatial relevance.
Abstract: Massive amount of data that are associated with geographic information are generated in Internet. More and more researches focus on how to retrieve geo-textual data effectively. Existing methods mostly allow exact matches for query keywords but fail to support fuzzy preference queries. In this paper, we study the skyline problem of fuzzy preference queries. That is, given a set of geo-textual data, the skyline comprises the objects that are not dominated by others. In this paper, we only consider the problem of two dimensions, the text relevance dimension and the spatial relevance dimension. We introduce two functions to quantify the text relevance and the spatial relevance. We also develop a new index structure to organize the geo-textual data and an algorithm based on it. Theoretical analysis and experimental results show that our method offers scalability and good performance.
TL;DR: This paper presents an approach that efficiently evaluates skyline queries in incomplete database by reducing the number of pairwise comparisons and shortens the searching space in identifying the skylines and illustrates that the approach is scalable and efficient.
01 Oct 2016
TL;DR: A novel approach for skyline detection is introduced, which adheres to a learning-based paradigm and exploits the representation power of deep architectures to improve the horizon line detection accuracy.
Abstract: Automatic skyline detection from mountain pictures is an important task in many applications, such as web image retrieval, augmented reality and autonomous robot navigation. Recent works addressing the problem of Horizon Line Detection (HLD) demonstrated that learning-based boundary detection techniques are more accurate than traditional filtering methods. In this paper we introduce a novel approach for skyline detection, which adheres to a learning-based paradigm and exploits the representation power of deep architectures to improve the horizon line detection accuracy. Differently from previous works, we explore a novel deconvolutional architecture, which introduces intermediate levels of supervision to support the learning process. Our experiments, conducted on a publicly available dataset, confirm that the proposed method outperforms previous learning-based HLD techniques by reducing the number of spurious edge pixels.
12 Dec 2016
TL;DR: This work proposes a novel approach to compute the skyline in a multi-parties computing environment without disclosing individual values of objects to another party, designed to work in MapReduce framework − in Hadoop framework.
Abstract: To select representative objects from a large scale database is an important step to understand the database. A skyline query, which retrieves a set of non-dominated objects, is one of popular methods for selecting representative objects. In this paper, we have considered a distributed algorithm for computing a skyline query in order to handle “big data”. In conventional distributed algorithms for computing a skyline query, the values of each object of a local database have to be disclosed to another. Recently, we have to be aware of privacy in a database, in which such disclosures of privacy information in conventional distributed algorithms are not allowed. In this work, we propose a novel approach to compute the skyline in a multi-parties computing environment without disclosing individual values of objects to another party. Our method is designed to work in MapReduce framework − in Hadoop framework. Our experimental results confirm the effectiveness and scalability of the proposed secure skyline computation.
TL;DR: In this article, a spatio-textual skyline (STS) query is proposed, in which the skyline points are selected not only based on their distances to a set of query locations, but also based on the relevance of query keywords.
Abstract: We study the modeling and evaluation of a spatio-textual skyline (STS) query, in which the skyline points are selected not only based on their distances to a set of query locations, but also based on their relevance to a set of query keywords. STS is especially relevant to modern applications, where points of interest are typically augmented with textual descriptions. We investigate three models for integrating textual relevance into the spatial skyline. Among them, model STD, which combines spatial distance with textual relevance in a derived dimensional space, is found to be the most effective one. STD computes a skyline which not only satisfies the intent of STS, but also has a small and easy-to-interpret size. We propose an efficient algorithm for computing STD-based skylines, which operates on an IR-tree that indexes the data. The effectiveness of our STD model and the efficiency of the proposed algorithm are evaluated on real data sets.
TL;DR: The bestpro-skyline query is introduced, which extends the dominance principle to also include the skyline probability of the probabilistic skyline tuples, and this approach results in pruning the result set to just a very small number of the most interesting probabilism skyline Tuples without the need to set any user-defined threshold.
01 Jun 2016
TL;DR: This work designs a system to solve the composition problem with two separate processes, the Graphplan approach and the database approach, and concludes that the solution returned by this approach always has fewer redundant services with a better QoS value.
Abstract: Web service composition enables the provision of existing resources on the web without investing in new infrastructure. However, searching an optimal composition solution with both functional and non-functional requirements is a computationally demanding problem: the time and space requirements may be insufferable due to the high number of available services. To alleviate this problem, we propose the application of a skyline operation to reduce the search space and improve the scalability. We design a system to solve the composition problem with two separate processes. The Graphplan approach finds a solution in a short time, the database approach may take longer time to find a solution, but the solution returned by this approach always has fewer redundant services with a better QoS value. Full Solution Indexing using Database (FSIDB) approach pre-computes all services combinations and store them as paths in a database. Partial pre-composing approach chooses "popular" paths generated by FSIDB approach and store them in a separate table. If the problem can be solved by these paths, there is no need to search the table with whole paths. We evaluate our approach with a web service challenge dataset.
Book•
12 May 2016
TL;DR: The Manhattan skyline is one of the great wonders of the modern world. But how and why did it form? Much has been written about the city's architecture and its general history, but little work has explored the economic forces that created the skyline as discussed by the authors.
Abstract: The Manhattan skyline is one of the great wonders of the modern world. But how and why did it form? Much has been written about the city's architecture and its general history, but little work has explored the economic forces that created the skyline. This book chronicles the economic history of the Manhattan skyline. In the process, the book debunks some widely-held misconceptions about the city's history. Part I lays out the historical and environmental background that established Manhattan's real estate trajectory before the Skyscraper Revolution at the end of the 19th century. The book begins with Manhattan's natural and geological history and then moves on to how it influenced early land use and neighborhood formation, and how these early decisions eventually impacted the location of skyscrapers. Part II focuses specifically on the economic history of skyscrapers and the skyline, investigating the reasons for their heights, frequencies, locations, and shapes. The book discusses why skyscrapers emerged downtown and why they appeared three miles to the north in midtown, but not in between. Contrary to popular belief it was not due to the depths of Manhattan's bedrock, nor the presence of Grand Central Station. Rather midtown's emergence was a response to the economic and demographic forces that were taking place north of 14th Street after the Civil War. The book also presents the first rigorous investigation of the causes of the building boom during the Roaring Twenties. Contrary to conventional wisdom, the boom was largely a rational response to the economic growth of the nation and city. The last chapter investigates the value of Manhattan Island and the relationship between skyscrapers and land prices. Finally, an Epilogue offers policy recommendations for a resilient and robust future skyline. Available in OSO:
Proceedings Article•
01 Jan 2016TL;DR: The maximum coverage representative skyline is introduced, which returns the k points collectively dominating the largest area of the data space, and reflects a critical property of the skyline itself.
Abstract: Skyline queries represent a dataset by the points on its pareto frontier, but can become very large. To alleviate this problem, representative skylines select exactly k skyline points. However, existing approaches are not scaleinvariant, not stable, or must materialise the entire skyline. We introduce the maximum coverage representative skyline, which returns the k points collectively dominating the largest area of the data space. It satisfies the above properties and reflects a critical property of the skyline itself.
25 Feb 2016
TL;DR: In the past three or four years, a new form of visualising those changes has become commonplace. On the billboards of almost every building site, a digital visualisation of what that site will look like when the construction work has finished.
Abstract: The urban fabric of global cities is constantly changing. And in the past three or four years, a new form of visualising those changes has become commonplace. On the billboards of almost every building site, a new kind of image is appearing: a digital visualisation of what that site will look like when the construction work has finished (see Figure 1). In particular, the iconic new buildings which no global city can now be without (Kaika 2011) are always surrounded by such visualisations on the hoardings that encircle them as they gradually rise into the city skyline. In the hustle and bustle of many big city streets, the presence of these high-definition, glossy visualisations is often striking, inviting passing pedestrians, passengers and drivers to pause and experience their high-end design, lovely weather, pretty planting, gorgeous lighting and leisured lifestyle.
TL;DR: A parallelization of the eager algorithm based on the notion of Skyline Influence Time, a parallel implementation for multicore architectures, and optimizations of the reduce phase and load‐balancing strategies to achieve near‐optimal speedup are proposed.
Abstract: The emergence of real-time decision-making applications in domains like high-frequency trading, emergency management, and service level analysis in communication networks has led to the definition of new classes of queries. Skyline queries are a notable example. Their results consist of all the tuples whose attribute vector is not dominated in the Pareto sense by one of any other tuple. Because of their popularity, skyline queries have been studied in terms of both sequential algorithms and parallel implementations for multiprocessors and clusters. Within the Data Stream Processing paradigm, traditional database queries on static relations have been revised in order to operate on continuous data streams. Most of the past papers propose sequential algorithms for continuous skyline queries, whereas there exist very few works targeting implementations on parallel machines. This paper contributes to fill this gap by proposing a parallel implementation for multicore architectures. We propose i a parallelization of the eager algorithm based on the notion of Skyline Influence Time, ii optimizations of the reduce phase and load-balancing strategies to achieve near-optimal speedup, and iii a set of experiments with both synthetic benchmarks and a real dataset in order to show our implementation effectiveness. Copyright © 2016 John Wiley & Sons, Ltd.