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Visual exploration has become an integral part of big spatial data management. With the increase in volume and number of spatial datasets, several specialized mechanisms have been proposed to speed up the exploration of these datasets. However, the existing techniques have major limitations which make them incapable of providing visual exploration for hundreds of thousands of big datasets on a single machine. This paper introduces a new index structure, termed AID*, that facilitates the visual exploration of an arbitrarily large number of big spatial datasets on a single machine. The AID* index defines multi-resolution fixed-size tiles on the input and classifies them as image, data, shallow, or empty tiles, based on their processing cost. Then, it uses this classification to build an index with a minimal index size and construction time, while supporting the desired real-time exploration interface. The index is constructed in parallel, using Hadoop or Spark, and is accessible to end users through a standard web interface similar to Google Maps. The small size of the index allows a single-machine server to host arbitrarily many datasets. Our experiments, on up-to 1 TB of data and 27 billion records, show that the construction of the proposed index is up-to an order of magnitude faster than the baselines without compromising the end-user interactivity. [ABSTRACT FROM AUTHOR]

Emergency Medical Services (EMS) are of great importance to saving people's lives from emergent accidents and diseases by efficiently picking up patients using ambulances. The transporting capability of an EMS system (e.g., defined as the average pickup time of patients) significantly depends on the real-time redeployment strategy of ambulances. That is, which station should an ambulance be redeployed to, after it becomes available (after it transports a patient to a hospital or after it finishes the in-site treatment for a patient)? However, it is a challenging task concerning with the multiple data D1-D5 as detailed in Introduction. To this end, in this paper, we propose a data-driven real-time ambulance redeployment approach that redeploys an ambulance to a proper station after it becomes available, so as to optimize the transporting capability of an EMS system, considering the aforementioned multiple data D1-D5. Specifically, the proposed approach is comprised of two stages to well consider the D1-D5. First, we propose a method (a safety time-based urgency index) to incorporate D1, D2, and D3 into each ambulance station's urgency degree (D*). Second, we propose an optimal matching algorithm to combine D*, D4, and D5 into the redeployment of the current available ambulance. Experimental results using data collected in real world demonstrate the significant advantages of our approach over many baselines. Comparing with baselines, our approach can save $\sim$ ∼ 4 minutes ($\sim$ ∼ 35 percent) of the average pickup time for each patient, improve the ratio of patients picked up within 10 minutes from 0.684 and 0.803 ($\sim$ ∼ 17 percent), and largely enhance the survival rate of patients ($\sim$ ∼ 12 percent for patients in category A1 and $\sim$ ∼ 17 percent for patients in A2). [ABSTRACT FROM AUTHOR]

In Australia and many other countries, distribution network service providers (DNSPs) have an obligation to their customers to provide electrical power that is reliable and of high quality. Failure to do so may have significant implications ranging from financial penalties theoretically through to the loss of a license to distribute electricity. In order to ensure the reliability and quality of supply are met, DNSPs engage in monitoring and reporting practice. This paper provides an overview of a large long-running power-quality monitoring project that has involved most of Australia's DNSPs at one time or another. This paper describes the challenges associated with conducting the project as well as some of the important outcomes and lessons learned. A number of novel reporting techniques that have been developed as part of the monitoring project are also presented. A discussion about large-volume data management, and issues related to reporting requirements in future distribution networks is included. [ABSTRACT FROM PUBLISHER]

Group skyline query is a powerful tool for optimal group analysis. Most of the existing group skyline queries select optimal groups by comparing the dominance relationship between aggregate-based points; such feature creates difficulties for users to specify an appropriate aggregate function. Besides, many significant groups that have great attractions to users in practice may be overlooked. To address these issues, the group skyline (GSky) query is formulated on the basis of a general definition of group dominance operator. While the existing GSky query algorithms are effective, there is still room for improvement in terms of progressiveness and efficiency. In this paper, we propose some new lemmas which facilitate direct generation of the GSky query results. Consecutively, we design a layered unit-based (LU) algorithm that applies a layered optimum strategy. Additionally, for the GSky query over the data that are dynamically produced and cannot be indexed, we propose a novel index-independent algorithm, called sorted-based progressive (SP) algorithm. The experimental results demonstrate the effectiveness, efficiency, and progressiveness of the proposed algorithms. By comparing with the state-of-the-art algorithm for the GSky query, our LU algorithm is more scalable and two orders of magnitude faster. [ABSTRACT FROM AUTHOR]

Hardware transactional memory (HTM) is an emerging hardware feature. HTM simplifies the programming model of concurrent programs while preserving high and scalable performance. With the commercial availability of HTM-capable processors, HTM has recently been adopted to construct efficient concurrent index structures. However, with the expansion of data volume and user amount, data management systems have to process workloads exhibiting high contention; meanwhile, according to our experiments, the conventional HTM-base concurrent index structures fail to provide scalable performance under highly-contented workloads. Such performance pathology strictly constrains the usage of HTM on data management systems. In this paper, we first conduct a thorough analysis on HTM-based concurrent index structures, and uncover several reasons for excessive HTM aborts incurred by both false and true conflicts under contention. Based on the analysis, we advocate Eunomia, a design pattern for HTM-based concurrent index structure which contains several principles to improve HTM performance, including splitting HTM regions with version-based concurrency control to reduce HTM working sets, partitioned data layout to reduce false conflicts, proactively detecting and avoiding conflicting requests, and adaptive concurrency control strategy. To validate their effectiveness, we apply such design principles to construct a scalable concurrent B+Tree and a skip list using HTM. Evaluation using key-value store and database benchmarks on a 20-core HTM-capable multi-core machine shows that Eunomia leads to substantial speedup under high contention, while incurring small overhead under low contention. [ABSTRACT FROM AUTHOR]

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 κ favorite probabilistic products (TFPP) query, is presented. The TFPP query is utilized to select κ 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. [ABSTRACT FROM PUBLISHER]

Existing methods have addressed the issue of handling each individual skyline query performed on data sets with partially ordered domains. However, it is still very challenging to process such queries for on-line applications with low response time. In this paper, we introduce a cache-based framework, called CSS, for further reducing the query processing time to support high-responsive applications. Skyline queries that were previously processed with user preferences similar to those of the current query contribute useful candidate result points. Hence, the answered queries are cached with both their results and user preferences such that the query processor can rapidly retrieve the result for a new query only from the result sets of selected queries with compatible user preferences. We introduce a similarity measure that establishes the level of similarity between the user preferences of a new query and a cached query; hence the system can start with the most similar candidates. Furthermore, if a new query is only partially answerable from the cache, then the query processor utilizes the partial result sets and performs less expensive constraint skyline queries guided by violated preferences. Furthermore, we introduce two access methods for cached queries indexed by their user preferences to only access a set of relevant cached queries for similarity measures. Extensive experiments are presented to demonstrate the performance and utility of our novel approach. [ABSTRACT FROM AUTHOR]

Dealing with both dense and sparse data in parallel environments usually leads to two different approaches: To rely on a monolithic, hard-to-modify parallel library, or to code all data management details by hand. In this paper we propose a third approach, that delivers good performance while the underlying library structure remains modular and extensible. Our solution integrates dense and sparse data management using a common interface, that also decouples data representation, partitioning, and layout from the algorithmic and parallel strategy decisions of the programmer. Our experimental results in different parallel environments show that this new approach combines the flexibility obtained when the programmer handles all the details with a performance comparable to the use of a state-of-the-art, sparse matrix parallel library. [ABSTRACT FROM AUTHOR]