Computation Of Queries Research Articles

Probabilistic n-of-N skyline computation over uncertain data streams

Skyline operator is a useful tool in multi-criteria decision making in various applications. Uncertainty is inherent in real applications due to various reasons. In this paper, we consider the problem of efficiently computing probabilistic skylines against the most recent N uncertain elements in a data stream seen so far. Specifically, we study the problem in the n-of-N model; that is, computing the probabilistic skyline for the most recent n (??n?≤?N) elements, where an element is a probabilistic skyline element if its skyline probability is not below a given probability threshold q. Firstly, an effective pruning technique to minimize the number of uncertain elements to be kept is developed. It can be shown that on average storing only O(logdN) uncertain elements from the most recent N elements is sufficient to support the precise computation of all probabilistic n-of-N skyline queries in a d-dimension space if the data distribution on each dimension is independent. A novel encoding scheme is then proposed together with efficient update techniques so that computing a probabilistic n-of-N skyline query in a d-dimension space is reduced to O(dloglogN?+?s) if the data distribution is independent, where s is the number of skyline points. A trigger based technique is provided to process continuous n-of-N skyline queries. Extensive experiments demonstrate that the new techniques on uncertain data streams can support on-line probabilistic skyline query computation over rapid data streams.

An Efficient Architecture for Parallel Skyline Computation over Large Distributed Datasets

The skyline query has been received much attention as an important operator in database systems for multi-preference analysis and decision making. Most of the previous works have focused on processing skyline queries on centralized data sets. However, the related data in real applications are practically scattered at several different servers. The skyline query computation in distributed environments is needed to gather a large number of data from the connected servers. The existing methods for a skyline query in distributed environments have two problems: (i) They have slow processing time for a skyline query. (ii) Most of the transferred data among servers in the network are unnecessary. In this paper, we propose a multi-layer grid method for efficiently processing a skyline query in distributed environments. The proposed method minimizes the unnecessary transferred data by using the grid mechanism. Experiments based on synthetic data sets and real data sets show that our proposed method outperforms the existing methods.

Scheduling in Heterogeneous Computing Environments for Proximity Queries

We present a novel, linear programming (LP)-based scheduling algorithm that exploits heterogeneous multicore architectures such as CPUs and GPUs to accelerate a wide variety of proximity queries. To represent complicated performance relationships between heterogeneous architectures and different computations of proximity queries, we propose a simple, yet accurate model that measures the expected running time of these computations. Based on this model, we formulate an optimization problem that minimizes the largest time spent on computing resources, and propose a novel, iterative LP-based scheduling algorithm. Since our method is general, we are able to apply our method into various proximity queries used in five different applications that have different characteristics. Our method achieves an order of magnitude performance improvement by using four different GPUs and two hexa-core CPUs over using a hexa-core CPU only. Unlike prior scheduling methods, our method continually improves the performance, as we add more computing resources. Also, our method achieves much higher performance improvement compared with prior methods as heterogeneity of computing resources is increased. Moreover, for one of tested applications, our method achieves even higher performance than a prior parallel method optimized manually for the application. We also show that our method provides results that are close (e.g., 75 percent) to the performance provided by a conservative upper bound of the ideal throughput. These results demonstrate the efficiency and robustness of our algorithm that have not been achieved by prior methods. In addition, we integrate one of our contributions with a work stealing method. Our version of the work stealing method achieves 18 percent performance improvement on average over the original work stealing method. This result shows wide applicability of our approach.

Parallel computation of skyline and reverse skyline queries using mapreduce

The skyline operator and its variants such as dynamic skyline and reverse skyline operators have attracted considerable attention recently due to their broad applications. However, computations of such operators are challenging today since there is an increasing trend of applications to deal with big data. For such data-intensive applications, the MapReduce framework has been widely used recently.In this paper, we propose efficient parallel algorithms for processing the skyline and its variants using MapReduce. We first build histograms to effectively prune out nonskyline (non-reverse skyline) points in advance. We next partition data based on the regions divided by the histograms and compute candidate (reverse) skyline points for each region independently using MapReduce. Finally, we check whether each candidate point is actually a (reverse) skyline point in every region independently. Our performance study confirms the effectiveness and scalability of the proposed algorithms.

Efficient All Top-k Computation - A Unified Solution for All Top-k, Reverse Top-k and Top-m Influential Queries

Given a set of objects P and a set of ranking functions F over P, an interesting problem is to compute the top ranked objects for all functions. Evaluation of multiple top-k queries finds application in systems, where there is a heavy workload of ranking queries (e.g., online search engines and product recommendation systems). The simple solution of evaluating the top-k queries one-by-one does not scale well; instead, the system can make use of the fact that similar queries share common results to accelerate search. This paper is the first, to our knowledge, thorough study of this problem. We propose methods that compute all top-k queries in batch. Our first solution applies the block indexed nested loops paradigm, while our second technique is a view-based algorithm. We propose appropriate optimization techniques for the two approaches and demonstrate experimentally that the second approach is consistently the best. Our approach facilitates evaluation of other complex queries that depend on the computation of multiple top-k queries, such as reverse top-k and top-m influential queries. We show that our batch processing technique for these complex queries outperform the state-of-the-art by orders of magnitude.

A comparative study of two approaches for supporting optimal network location queries

Given a set S of sites and a set O of weighted objects, an optimal location query finds the location(s) where introducing a new site maximizes the total weight of the objects that are closer to the new site than to any other site. With such a query, for instance, a franchise corporation (e.g., McDonald's) can find a location to open a new store such that the number of potential store customers (i.e., people living close to the store) is maximized. Optimal location queries are computationally complex to compute and require efficient solutions that scale with large datasets. Previously, two specific approaches have been proposed for efficient computation of optimal location queries. However, they both assume p-norm distance (namely, L1 and L2/Euclidean); hence, they are not applicable where sites and objects are located on spatial networks. In this article, we focus on optimal network location (ONL) queries, i.e., optimal location queries in which objects and sites reside on a spatial network. We introduce two complementary approaches, namely EONL (short for Expansion-based ONL) and BONL (short for Bound-based ONL), which enable efficient computation of ONL queries with datasets of uniform and skewed distributions, respectively. Moreover, with an extensive experimental study we verify and compare the efficiency of our proposed approaches with real world datasets, and we demonstrate the importance of considering network distance (rather than p-norm distance) with ONL queries.

613 Hepatitis C Virus (HCV) Diagnosis and Clinical Outcomes of Patients With HCV-Related Hepatocellular Carcinoma (HCC): A Comparative Study of Asians Versus Non-Asians

BACKGROUND AND AIMS: In addition to hepatitis B virus, HCV is also an important cause of HCC in Asians; however, it is often overlooked. This study aims to examine baseline characteristics, timing of HCV diagnosis and long-term survival of HCV-related HCC in Asians compared to non-Asian patients. METHODS: We conducted a retrospective cohort study of 798 consecutive Asian (n=220) and non-Asian (n=572) patients with HCV-related HCC who were identified via computer query using ICD-9 diagnosis at a U.S. university medical center between 7/1996 and 6/2012. Individual records were reviewed. RESULTS: Asians were much older (66 [38-88] vs. 56 [31-87] years, P ,0.0001) and more likely to be female (33% vs. 19%, P,0.0001). A significantly larger proportion of Asians were also diagnosed with HCC within only 2 years of HCV diagnosis compared to non-Asians (35% vs. 20%, p=0.001). Non-Asians were more likely to have decompensated liver disease and had higher median Child-Turcotte-Pugh score (6 [5-11] vs. 7 [5-13], P ,0.0001). Asian patients were more likely to undergo liver-directed palliative therapy (46% vs. 28%) and much less likely to be listed for liver transplantation(20% vs. 48%) (P ,0.001), despite similar rates of meeting Milan criteria for liver transplantation (52 vs.58%, P=0.16). Overall, there was a trend for higher median survival rates in Asians compared to non-Asians (30 vs. 21 months, P=0.091). Among those who were listed for liver transplantation, there was no statistically significant difference in survival between Asians and non-Asians in the first 2 years (72% vs. 68%) but there was divergence with Asians having higher survival afterwards (61% vs. 51%) (Figure 1). However, among those undergoing liver-directed palliative therapy, Asians had significantly higher long-term survival compared to non-Asians (5-year survival: 28% vs. 10%, P,0.0001) (Figure 2). On multivariate Cox proportional hazards model also inclusive of age, sex, BCLC staging, Child-Pugh score, meeting Milan criteria, liver transplantation listing, non-Asian ethnicity was an independent predictor for lower overall survival (HR=0.70 [0.52-0.86]). CONCLUSIONS: Despite being older and having a lower rate of liver transplantation listing, Asian HCV/HCC patients had higher median survival compared to non-Asian patients, particularly among those undergoing palliative therapy. Non-Asians were more likely to have decompensated liver disease and non-Asian ethnicity was an independent predictor for poorer long-term survival. Compared to non-Asians, Asians with HCV-related HCCwere also muchmore likely to have delayed HCV diagnosis. Improved strategies in HCV screening in Asians are needed as it may lead to earlier diagnosis and treatment of HCV infection and possible prevention of HCC development in this ethnic population with a disproportionate HCC disease burden.

616 C-FOS Mediated Regulation of the Apical Sodium Dependent Bile Acid Transporter (ASBT) Plays a Critical Role in the Ileal Response to FGF-19

BACKGROUND AND AIMS: In addition to hepatitis B virus, HCV is also an important cause of HCC in Asians; however, it is often overlooked. This study aims to examine baseline characteristics, timing of HCV diagnosis and long-term survival of HCV-related HCC in Asians compared to non-Asian patients. METHODS: We conducted a retrospective cohort study of 798 consecutive Asian (n=220) and non-Asian (n=572) patients with HCV-related HCC who were identified via computer query using ICD-9 diagnosis at a U.S. university medical center between 7/1996 and 6/2012. Individual records were reviewed. RESULTS: Asians were much older (66 [38-88] vs. 56 [31-87] years, P ,0.0001) and more likely to be female (33% vs. 19%, P,0.0001). A significantly larger proportion of Asians were also diagnosed with HCC within only 2 years of HCV diagnosis compared to non-Asians (35% vs. 20%, p=0.001). Non-Asians were more likely to have decompensated liver disease and had higher median Child-Turcotte-Pugh score (6 [5-11] vs. 7 [5-13], P ,0.0001). Asian patients were more likely to undergo liver-directed palliative therapy (46% vs. 28%) and much less likely to be listed for liver transplantation(20% vs. 48%) (P ,0.001), despite similar rates of meeting Milan criteria for liver transplantation (52 vs.58%, P=0.16). Overall, there was a trend for higher median survival rates in Asians compared to non-Asians (30 vs. 21 months, P=0.091). Among those who were listed for liver transplantation, there was no statistically significant difference in survival between Asians and non-Asians in the first 2 years (72% vs. 68%) but there was divergence with Asians having higher survival afterwards (61% vs. 51%) (Figure 1). However, among those undergoing liver-directed palliative therapy, Asians had significantly higher long-term survival compared to non-Asians (5-year survival: 28% vs. 10%, P,0.0001) (Figure 2). On multivariate Cox proportional hazards model also inclusive of age, sex, BCLC staging, Child-Pugh score, meeting Milan criteria, liver transplantation listing, non-Asian ethnicity was an independent predictor for lower overall survival (HR=0.70 [0.52-0.86]). CONCLUSIONS: Despite being older and having a lower rate of liver transplantation listing, Asian HCV/HCC patients had higher median survival compared to non-Asian patients, particularly among those undergoing palliative therapy. Non-Asians were more likely to have decompensated liver disease and non-Asian ethnicity was an independent predictor for poorer long-term survival. Compared to non-Asians, Asians with HCV-related HCCwere also muchmore likely to have delayed HCV diagnosis. Improved strategies in HCV screening in Asians are needed as it may lead to earlier diagnosis and treatment of HCV infection and possible prevention of HCC development in this ethnic population with a disproportionate HCC disease burden.

614 The Oncogenic Role of Praja1 on Disrupting TGF-β Signaling in Hepatocellular Cancer

BACKGROUND AND AIMS: In addition to hepatitis B virus, HCV is also an important cause of HCC in Asians; however, it is often overlooked. This study aims to examine baseline characteristics, timing of HCV diagnosis and long-term survival of HCV-related HCC in Asians compared to non-Asian patients. METHODS: We conducted a retrospective cohort study of 798 consecutive Asian (n=220) and non-Asian (n=572) patients with HCV-related HCC who were identified via computer query using ICD-9 diagnosis at a U.S. university medical center between 7/1996 and 6/2012. Individual records were reviewed. RESULTS: Asians were much older (66 [38-88] vs. 56 [31-87] years, P ,0.0001) and more likely to be female (33% vs. 19%, P,0.0001). A significantly larger proportion of Asians were also diagnosed with HCC within only 2 years of HCV diagnosis compared to non-Asians (35% vs. 20%, p=0.001). Non-Asians were more likely to have decompensated liver disease and had higher median Child-Turcotte-Pugh score (6 [5-11] vs. 7 [5-13], P ,0.0001). Asian patients were more likely to undergo liver-directed palliative therapy (46% vs. 28%) and much less likely to be listed for liver transplantation(20% vs. 48%) (P ,0.001), despite similar rates of meeting Milan criteria for liver transplantation (52 vs.58%, P=0.16). Overall, there was a trend for higher median survival rates in Asians compared to non-Asians (30 vs. 21 months, P=0.091). Among those who were listed for liver transplantation, there was no statistically significant difference in survival between Asians and non-Asians in the first 2 years (72% vs. 68%) but there was divergence with Asians having higher survival afterwards (61% vs. 51%) (Figure 1). However, among those undergoing liver-directed palliative therapy, Asians had significantly higher long-term survival compared to non-Asians (5-year survival: 28% vs. 10%, P,0.0001) (Figure 2). On multivariate Cox proportional hazards model also inclusive of age, sex, BCLC staging, Child-Pugh score, meeting Milan criteria, liver transplantation listing, non-Asian ethnicity was an independent predictor for lower overall survival (HR=0.70 [0.52-0.86]). CONCLUSIONS: Despite being older and having a lower rate of liver transplantation listing, Asian HCV/HCC patients had higher median survival compared to non-Asian patients, particularly among those undergoing palliative therapy. Non-Asians were more likely to have decompensated liver disease and non-Asian ethnicity was an independent predictor for poorer long-term survival. Compared to non-Asians, Asians with HCV-related HCCwere also muchmore likely to have delayed HCV diagnosis. Improved strategies in HCV screening in Asians are needed as it may lead to earlier diagnosis and treatment of HCV infection and possible prevention of HCC development in this ethnic population with a disproportionate HCC disease burden.

Semantic query optimization in the presence of types

Both semantic and type-based query optimization rely on the idea that queries may exhibit non-trivial rewritings if the state space of the database is restricted. While these two problems have always been studied as separate problems in previous work, in this paper we present a unifying, logic-based query optimization framework that builds upon the classical chase algorithm and brings both problems together. As a major challenge, our novel setting requires chasing conjunctive queries with union and negation in the presence of dependencies containing negation and disjunction. Tackling this problem, we study the applicability of the chase in this setting, develop novel conditions that guarantee its termination, identify fragments for which minimal query computation (w.r.t. a generic cost function) is always possible, and investigate the complexity of related decision problems.

Real-time data warehouse: a solution and evaluation

Real-time warehousing has been researched and tried in practice by several companies, but there should be a definition of an architecture and an evaluation of its merits. Based on both industry and theoretical insights, we define a data warehouse architecture for constant integration of data without compromising query performance, and we evaluate its capacity to provide realtime. For this real-time data warehouse RTDW we define a dynamic warehouse component and a static warehouse component to represent the recently integrated data and the rest of the data, respectively, with relevant choices concerning how the components merge together. We propose design choices concerning query computation mechanisms and evaluate the alternatives to conclude which is the most efficient implementation of those mechanisms. The real-time data warehouse architecture is evaluated with a real-time benchmark setup that considers online loading with simultaneous query workload processing. Results prove the validity of the architecture, compare different mechanisms and quantify its efficiency in the constant integration context. The performance is carefully analysed taking into account several important factors.

Point-in-Polygon Test Method Based on Center Points of Grid

提出一种基于均匀网格的点在多边形内的高效判定算法.它首先建立均匀网格,并从左至右依次计算每个网格单元中心点的位置属性.每个单元中心点的位置属性直接依据其左侧邻接单元已知位置属性的中心点快速获得.在判定点的位置时,确定被测点所在单元,并依据该单元中心点的位置属性判定被测点的位置属性.由于预处理和判定时均利用邻近点的已知位置属性来确定未知点位置属性,可以很好地进行局部化的计算.因此,新方法比现有方法快很多,并且其预处理时间复杂度也由同类网格算法的O(N^3/2)下降为O(N).同时,新方法可以统一处理含有自相交及重叠边的非流形多边形.实验结果表明,相比于其他基于均匀网格的方法,新方法可将预处理的速度提高几倍,将判断计算的速度提高十几到几十倍.其速度甚至优于具有该问题最低判定计算时间复杂度O(logN)的基于凸剖分的判定算法.;The paper proposes an efficient point-in-polygon test method based on uniform grids. In preprocessing, it constructs uniform grids and processes the center points in a sequence to determine whether they are inside the polygon, when the center points with known inclusion properties are used to quickly determine their adjacent center points. When performing an inclusion query, it first finds the cell that contains the query point and then produces a line segment from the query point to the cell’s center point and counts the edges crossed by the line segment for determination. As both the preprocessing and the inclusion tests make use of known information to determine the center points or query points, the computation can run locally for acceleration and the new method can run much faster than existing methods. In most cases, it can reduce the preprocessing time complexity from O(N^3/2) to O(N) for the methods based on grids, where N is the number of the polygon edges. Meanwhile, the new method can efficiently treat the non-manifold polygons with self-intersection or overlapping edges. Experimental results show that compared with other similar methods based on uniform grids, the new algorithm can speed up the preprocessing by several times and the query computation by one or several dozens of times, and can move faster than the method by convex decomposition, which has the lowest complexity O(logN) for point-in- polygon tests.

SOME INITIAL THOUGHTS ON BOUNDED QUERY COMPUTATIONS OVER THE REALS

A classical theme in recursion theory is the question whether for a set A and n given elements x1,…,xn, an oracle machine having access to an oracle B can determine which of the elements xi belong to A. And if yes, how many queries are necessary? Here, B = A is possible and leads to interesting special cases of the general question In the present paper we adapt classical notions in this area of bounded query computations to real number algorithms as formalized by Blum, Shub, and Smale and analyze them in the new framework. Among the results obtained we find: A real version of a non-speedup theorem based on real quantifier elimination, some basic properties about selective real sets, and a way to construct natural terse sets in ℝ by applying the implicit function theorem. The purpose of the paper is on presenting some interesting questions and basic results as an appertizer to intensify research into this direction.

Mining neighbor-based patterns in data streams

Discovery of complex patterns such as clusters, outliers, and associations from huge volumes of streaming data has been recognized as critical for many application domains. However, little research effort has been made toward detecting patterns within sliding window semantics as required by real-time monitoring tasks, ranging from real time traffic monitoring to stock trend analysis. Applying static pattern detection algorithms from scratch to every window is impractical due to their high algorithmic complexity and the real-time responsiveness required by streaming applications. In this work, we develop methods for the incremental detection of neighbor-based patterns, in particular, density-based clusters and distance-based outliers over sliding stream windows. Incremental computation for pattern detection queries is challenging. This is because purging of to-be-expired data from previously formed patterns may cause birth, shrinkage, splitting or termination of these complex patterns. To overcome this, we exploit the “predictability” property of sliding windows to elegantly discount the effect of expired objects with little maintenance cost. Our solution achieves guaranteed minimal CPU consumption, while keeping the memory utilization linear in the number of objects in the window. To thoroughly analyze the performance of our proposed methods, we develop a cost model characterizing the performance of our proposed neighbor-based pattern mining strategies. We conduct an analysis study to not only identify the key performance factors for each strategy but also show under which conditions each of them are most efficient. Our comprehensive experimental study, using both synthetic and real data from domains of moving object monitoring and stock trades, demonstrates superiority of our proposed strategies over alternate methods in both CPU processing resources and in memory utilization.

Indexing Uncertain Data in General Metric Spaces

In this study, we deal with the problem of efficiently answering range queries over uncertain objects in a general metric space. In this study, an uncertain object is an object that always exists but its actual value is uncertain and modeled by a multivariate probability density function. As a major contribution, this is the first work providing an effective technique for indexing uncertain objects coming from general metric spaces. We generalize the reverse triangle inequality to the probabilistic setting in order to exploit it as a discard condition. Then, we introduce a novel pivot-based indexing technique, called UP-index, and show how it can be employed to speed up range query computation. Importantly, the candidate selection phase of our technique is able to noticeably reduce the set of candidates with little time requirements. Finally, we provide a criterion to measure the quality of a set of pivots and study the problem of selecting a good set of pivots according to the introduced criterion. We report some intractability results and then design an approximate algorithm with statistical guarantees for selecting pivots. Experimental results validate the effectiveness of the proposed approach and reveal that the introduced technique may be even preferable to indexing techniques specifically designed for the euclidean space.

The Measuring of the Superheated Steam Thermal Flux Based on Artificial Neural Networks

superheated steam; thermal flow; neural network; simulation of computer Abstrct: It was established that the measuring system model of the superheated steam heat flux based on the Artificial Neural Networks technology, and the measureing of the thermal flow, the compensation of the pressure and the temperature of the superheated steam were achieved. The error analysis of the measuring system was made by using the computer. The online data collection, computation, storage, real-time display and history date query were realized, which make the system more live and reliable.

An exploration of SciDB in the context of emerging technologies for data stores in particle physics and cosmology

Traditional relational databases have not always been well matched to the needs of data-intensive sciences, but efforts are underway within the database community to attempt to address many of the requirements of large-scale scientific data management. One such effort is the open-source project SciDB. Since its earliest incarnations, SciDB has been designed for scalability in parallel and distributed environments, with a particular emphasis upon native support for array constructs and operations. Such scalability is of course a requirement of any strategy for large-scale scientific data handling, and array constructs are certainly useful in many contexts, but these features alone do not suffice to qualify a database product as an appropriate technology for hosting particle physics or cosmology data. In what constitutes its 1.0 release in June 2011, SciDB has extended its feature set to address additional requirements of scientific data, with support for user-defined types and functions, for data versioning, and more. This paper describes an evaluation of the capabilities of SciDB for two very different kinds of physics data: event-level metadata records from proton collisions at the Large Hadron Collider (LHC), and the output of cosmological simulations run on very-large-scale supercomputers. This evaluation exercises the spectrum of SciDB capabilities in a suite of tests that aim to be representative and realistic, including, for example, definition of four-vector data types and natural operations thereon, and computational queries that match the natural use cases for these data.

PHP91 Factors Affecting Serum Potassium Measurements After Initiation of Spironolactone at an Academic Medical Center

Serum potassium levels should be assessed periodically following the initiation of spironolactone treatment. This study examined the rate of laboratory follow-up and factors affecting it at an academic medical center. Adult patients starting spironolactone treatment between January 1, 2008 and September 30, 2010 were studied. Subjects were excluded if the indicated disease was not followed up at the medical center, or the exact date of the first spironolactone prescription was unknown. Data were collected by computer query and chart review. Survival analysis examined the factors predicting the time to first serum potassium testing after spironolactone initiation. A total of 161 patients were included (mean age 51.1 years, 57% female), 29% of whom had some renal impairment (creatinine clearance <60 ml/min). Spironolactone was most commonly used for ascites/edema due to primary liver disease (45.3%), followed by congestive heart failure (19.3%) and polycystic ovary syndrome or other hormone-related disorders (18.6%). The median time to first potassium test was 22 days. After adjusting for age, gender, renal function and insurance status, medical indication was the only factor impacting time to first potassium check-up. Compared to patients with primary liver disease, patients who used spironolactone for ascites/edema not due to hepatic or cardiac causes (e.g., non-hepatic malignancy, nephrotic syndrome) were likely to have a potassium level checked earlier (hazard ratio [HR]=2.2, 95% CI=1.04-4.6), whereas potassium testing was more likely to be delayed in patients with hormone-related disorders (HR=0.4, 95% CI=0.2-0.9) or hypertension (HR=0.4, 95% CI=0.2-0.8). Medical indication was significantly predictive of the time to first potassium check in patients receiving spironolactone. It is surprising that renal dysfunction was not a factor, while the association with indication has many possible explanations from increased routine lab testing for some indications and higher risks of hyperkalemia complications. Further studies are needed to validate and understand these data.

Accelerating large semantic web databases by parallel join computations of SPARQL queries

While a number of optimizing techniques have been developed to efficiently process increasing large Semantic Web databases, these optimization approaches have not fully leveraged the powerful computation capability of modern computers. Today's multi-core computers promise an enormous performance boost by providing a parallel computing platform. Although the parallel relational database systems have been well built, parallel query computing in Semantic Web databases have not extensively been studied. In this work, we develop the parallel algorithms for join computations of SPARQL queries. Our performance study shows that the parallel computation of SPARQL queries significantly speeds up querying large Semantic Web databases.

Efficient computation of queries on feature streams

This article introduces the notion of virtual feature stream , a feature stream defined from a primary data stream, in which at any time only the features that are needed to compute the queries that are currently running in the system are computed. Virtual feature streams are, in general, impossible to determine a priori, but the paper introduces an algorithm that stops the computation of features as soon as it can be proved that they are no longer needed thus generating, albeit in a roundabout and more expensive than the ideal way, a feature stream that is less expensive than the complete one to compute and safe: the queries that accept the virtual feature stream are those (and only those) that would accept the original feature stream.