45 research outputs found

    The Impact of Global Clustering on Spatial Database Systems

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    Global clustering has rarely been investigated in the area of spatial database systems although dramatic performance improvements can be achieved by using suitable techniques. In this paper, we propose a simple approach to global clustering called cluster organization. We will demonstrate that this cluster organization leads to considerable performance improvements without any algorithmic overhead. Based on real geographic data, we perform a detailed empirical performance evaluation and compare the cluster organization to other organization models not using global clustering. We will show that global clustering speeds up the processing of window queries as well as spatial joins without decreasing the performance of the insertion of new objects and of selective queries such as point queries. The spatial join is sped up by a factor of about 4, whereas non-selective window queries are accelerated by even higher speed up factors

    Grid File Approach to Large Multidimensional Dynamic Data Structures

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    Computing and Information Science

    Multidimensional Range Queries on Modern Hardware

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    Range queries over multidimensional data are an important part of database workloads in many applications. Their execution may be accelerated by using multidimensional index structures (MDIS), such as kd-trees or R-trees. As for most index structures, the usefulness of this approach depends on the selectivity of the queries, and common wisdom told that a simple scan beats MDIS for queries accessing more than 15%-20% of a dataset. However, this wisdom is largely based on evaluations that are almost two decades old, performed on data being held on disks, applying IO-optimized data structures, and using single-core systems. The question is whether this rule of thumb still holds when multidimensional range queries (MDRQ) are performed on modern architectures with large main memories holding all data, multi-core CPUs and data-parallel instruction sets. In this paper, we study the question whether and how much modern hardware influences the performance ratio between index structures and scans for MDRQ. To this end, we conservatively adapted three popular MDIS, namely the R*-tree, the kd-tree, and the VA-file, to exploit features of modern servers and compared their performance to different flavors of parallel scans using multiple (synthetic and real-world) analytical workloads over multiple (synthetic and real-world) datasets of varying size, dimensionality, and skew. We find that all approaches benefit considerably from using main memory and parallelization, yet to varying degrees. Our evaluation indicates that, on current machines, scanning should be favored over parallel versions of classical MDIS even for very selective queries

    Multidimensional access methods

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    New generic indexing technology

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    There has been no fundamental change in the dynamic indexing methods supporting database systems since the invention of the B-tree twenty-five years ago. And yet the whole classical approach to dynamic database indexing has long since become inappropriate and increasingly inadequate. We are moving rapidly from the conventional one-dimensional world of fixed-structure text and numbers to a multi-dimensional world of variable structures, objects and images, in space and time. But, even before leaving the confines of conventional database indexing, the situation is highly unsatisfactory. In fact, our research has led us to question the basic assumptions of conventional database indexing. We have spent the past ten years studying the properties of multi-dimensional indexing methods, and in this paper we draw the strands of a number of developments together - some quite old, some very new, to show how we now have the basis for a new generic indexing technology for the next generation of database systems

    Setup-Free Secure Search on Encrypted Data: Faster and Post-Processing Free

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    We present a novel secure search\textit{secure search} protocol on data and queries encrypted with Fully Homomorphic Encryption (FHE). Our protocol enables organizations (client) to (1) securely upload an unsorted data array x=(x[1],,x[n])x=(x[1],\ldots,x[n]) to an untrusted honest-but-curious sever, where data may be uploaded over time and from multiple data-sources; and (2) securely issue repeated search queries qq for retrieving the first element (i,x[i])(i^*,x[i^*]) satisfying an agreed matching criterion i=min {i[n]  IsMatch(x[i],q)=1}i^* = \min\ \left\{ \left.i\in[n] \;\right\vert \mathsf{IsMatch}(x[i],q)=1 \right\}, as well as fetching the next matching elements with further interaction. For security, the client encrypts the data and queries with FHE prior to uploading, and the server processes the ciphertexts to produce the result ciphertext for the client to decrypt. Our secure search protocol improves over the prior state-of-the-art for secure search on FHE encrypted data (Akavia, Feldman, Shaul (AFS), CCS\u272018) in achieving: (1) Post-processing free\textit{Post-processing free} protocol where the server produces a ciphertext for the correct search outcome with overwhelming success probability.This is in contrast to returning a list of candidates for the client to post-process, or suffering from a noticeable error probability, in AFS. Our post-processing freeness enables the server to use secure search as a sub-component in a larger computation without interaction with the client. (2) Faster protocol:\textit{Faster protocol:}(a) Client time and communication bandwidth are improved by a log2n/loglogn\log^2n/\log\log n factor. (b) Server evaluates a polynomial of degree linear in logn\log n (compare to cubic in AFS), and overall number of multiplications improved by up to logn\log n factor.(c) Employing only GF(2)\textrm{GF}(2) computations (compare to GF(p)\textrm{GF}(p) for p2p \gg 2 in AFS) to gain both further speedup and compatibility to all current FHE candidates. (3) Order of magnitude speedup exhibited by extensive benchmarks\textit{Order of magnitude speedup exhibited by extensive benchmarks} we executed on identical hardware for implementations of ours versus AFS\u27s protocols. Additionally, like other FHE based solutions, out solution is setup-free: to outsource elements from the client to the server, no additional actions are performed on xx except for encrypting it element by element (each element bit by bit) and uploading the resulted ciphertexts to the server

    Indexing for moving objects

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    Master'sMASTER OF SCIENC

    Stateful Multi-Client Verifiable Computation

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    This paper develops a cryptographic protocol for outsourcing arbitrary stateful computation among multiple clients to an untrusted server, while guaranteeing integrity of the data. The clients communicate only with the server and store only a short authenticator to ensure that the server does not cheat. Our contribution is two-fold. First, we extend the recent hash&prove scheme of Fiore et al. (CCS 2016) to stateful computations that support arbitrary updates by the untrusted server, in a way that can be verified by the clients. We use this scheme to generically instantiate authenticated data types. Second, we describe a protocol for multi-client verifiable computation based on an authenticated data type, and prove that it achieves a computational version of fork linearizability. This is the strongest guarantee that can be achieved in the setting where clients do not communicate directly; it ensures correctness and consistency of outputs seen by the clients individually

    Scalability analysis of declustering methods for multidimensional range queries

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    Abstract—Efficient storage and retrieval of multiattribute data sets has become one of the essential requirements for many data-intensive applications. The Cartesian product file has been known as an effective multiattribute file structure for partial-match and best-match queries. Several heuristic methods have been developed to decluster Cartesian product files across multiple disks to obtain high performance for disk accesses. Although the scalability of the declustering methods becomes increasingly important for systems equipped with a large number of disks, no analytic studies have been done so far. In this paper, we derive formulas describing the scalability of two popular declustering methods¦Disk Modulo and Fieldwise Xor¦for range queries, which are the most common type of queries. These formulas disclose the limited scalability of the declustering methods, and this is corroborated by extensive simulation experiments. From the practical point of view, the formulas given in this paper provide a simple measure that can be used to predict the response time of a given range query and to guide the selection of a declustering method under various conditions
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