Indexing metric uncertain data for range queries and range joins

Chen, Lu; Gao, Yunjun; Zhong, Aoxiao; Jensen, Christian S.; Chen, Gang; Zheng, Baihua

doi:10.1007/s00778-017-0465-6

Cited by 16 publications

(3 citation statements)

References 43 publications

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“…[37]. Solution for indexing of uncertain spatial [1,28] and spatiotemporal [114,47] data have been proposed to speed up various of the previously mentioned query types.…”

Section: Advanced Techniques For Managing Uncertain Spatial Datamentioning

confidence: 99%

Uncertain Spatial Data Management:An Overview

Zuefle

2020

Preprint

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Both the current trends in technology such as smart phones, general mobile devices, stationary sensors, and satellites as we as a new user mentality of using this technology to voluntarily share enriched location information produces a flood of geo-spatial and geo-spatio-temporal data. This data flood provides a tremendous potential of discovering new and useful knowledge. But in addition to the fact that measurements are imprecise, spatial data is often interpolated between discrete observations. To reduce communication and bandwidth utilization, data is often subjected to a reduction, thereby eliminating some of the known/recorded values. These issues introduce the notion of uncertainty in the context of spatio-temporal data management, an aspect raising imminent need for scalable and flexible solutions. The main scope of this chapter is to survey existing techniques for managing, querying, and mining uncertain spatio-temporal data. First, this chapter surveys common data representations for uncertain data, explains the commonly used possible worlds semantics to interpret an uncertain database, and surveys existing system to process uncertain data. Then this chapter defines the notion of different probabilistic result semantics to distinguish the task of enrich individual objects with probabilities rather than enriched entire results with probabilities. To distinguish between result semantics is important, as for many queries, the problem of computing object-level result probabilities can be done efficiently, whereas the problem of computing probabilities of entire results is often exponentially hard. Then, this chapter provides an overview over probabilistic query predicates to quantify the required probability of a result to be included in the result. Finally, this chapter introduces a novel paradigm to efficiently answer any kind of query on uncertain data: the Paradigm of Equivalent Worlds, which groups the exponential set of possible database worlds into a polynomial number of set of equivalent worlds that can be processed efficiently. Examples and use-cases of querying uncertain spatial data are provided using the example of uncertain range queries.

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“…[37]. Solution for indexing of uncertain spatial [1,28] and spatiotemporal [114,47] data have been proposed to speed up various of the previously mentioned query types.…”

Section: Advanced Techniques For Managing Uncertain Spatial Datamentioning

confidence: 99%

Uncertain Spatial Data Management:An Overview

Zuefle

2020

Preprint

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“…Typical indexes realize some form of balanced search tree or hash table [16,49]. Specific indexing techniques for uncertain data are also known, see for example [13,3,11,12]. Some research effort was also dedicated to the creation of a general architecture for indexing, see, for example, [29,33].…”

Section: State Of the Artmentioning

confidence: 99%

Overlay Indexes: Efficiently Supporting Aggregate Range Queries and Authenticated Data Structures in Off-the-Shelf Databases

Pennino

Pizzonia

Papi³

2019

IEEE Access

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Commercial off-the-shelf DataBase Management Systems (DBMSes) are highly optimized to process a wide range of queries by means of carefully designed indexing and query planning. However, many aggregate range queries are usually performed by DBMSes using sequential scans, and certain needs, like storing Authenticated Data Structures (ADS), are not supported at all. Theoretically, these needs could be efficiently fulfilled adopting specific kinds of indexing, which however are normally ruled-out in DBMSes design.We introduce the concept of overlay index : an index that is meant to be stored in a standard database, alongside regular data and managed by regular software, to complement DBMS capabilities. We show a data structure, that we call DB-tree, that realizes an overlay index to support a wide range of custom aggregate range queries as well as ADSes, efficiently. All DB-trees operations can be performed by executing a small number of queries to the DBMS, that can be issued in parallel in one or two query rounds, and involves a logarithmic amount of data. We experimentally evaluate the efficiency of DB-trees showing that our approach is effective, especially if data updates are limited.

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“…There is increasing interest in providing additional operations for modern applications, including iterators [1,32,33,35,36,37] or general range queries [6,9]. These are required in many big-data applications [11,26,34], where shared in-memory tree-based data indices must be created for fast data retrieval and useful data analytics. Prevalent programming frameworks (e.g., Java [23], .NET [31], TBB [22]) that provide concurrent data structures have added operations to support (non-linearizable) iterators.…”

Section: Introductionmentioning

confidence: 99%

Persistent Non-Blocking Binary Search Trees Supporting Wait-Free Range Queries

Fatourou

Papavasileiou

Ruppert

2019

The 31st ACM Symposium on Parallelism in Algorithms and Architectures

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This paper presents the first implementation of a search tree data structure in an asynchronous shared-memory system that provides a wait-free algorithm for executing range queries on the tree, in addition to non-blocking algorithms for Insert, Delete and Find, using single-word Compareand-Swap (CAS). The implementation is linearizable and tolerates any number of crash failures.Insert and Delete operations that operate on different parts of the tree run fully in parallel (without any interference with one another). We employ a lightweight helping mechanism, where each Insert, Delete and Find operation helps only update operations that affect the local neighbourhood of the leaf it arrives at. Similarly, a RangeScan helps only those updates taking place on nodes of the part of the tree it traverses, and therefore RangeScans operating on different parts of the tree do not interfere with one another. Our implementation works in a dynamic system where the number of processes may change over time.The implementation builds upon the non-blocking binary search tree implementation presented by Ellen et al. [13] by applying a simple mechanism to make the tree persistent. 15 type Internal { subtype of Node 16 Key ∪ {∞1, ∞2} key 17 Update update 18 Node *left, *right 19 Node *prev 20 int seq 21 } 22 type Leaf { subtype of Node 23 Key ∪ {∞1, ∞2} key 24 Update update 25 Node *prev 26 int seq 27 } 28Initialization: 29 shared counter Counter := 0 30 shared Info *Dummy := pointer to a new Info object whose state field is Abort, and whose other fields are ⊥ 31 shared Internal *Root := pointer to new Internal node with key field ∞2, update field Flag, Dummy , prev field ⊥, seq field 0, and its lef t and right fields pointing to new Leaf nodes whose prev fields are ⊥, seq fields are 0, and keys ∞1 and ∞2, respectively 33 Precondition: seq ≥ 0 Precondition: p is non-⊥ and p → seq ≤ seq 45 if lef t then l := p → left else l := p → right Move down to appropriate child 46 while (l → seq > seq) l := l → prev 47 return l; 48 } 49 ValidateLink(Internal *parent, Internal *child, Boolean lef t): Boolean, Update { 50 Preconditions: parent and child are non-⊥ 51 Update up 52 up := parent → update 53 if Frozen(up) then { 54 Help(up.inf o) 55 return False, ⊥ 56 } 57 if (lef t and child = parent → lef t) or (¬lef t and child = parent → right) then return False, ⊥ 58 else return True, up 59 } Boolean validated 64 validated, pupdate := ValidateLink(p, l, k < p → key) 65 if validated and p = Root then validated, gpupdate := ValidateLink(gp, p, k < gp → key) 66 validated := validated and p → update = pupdate and (p = Root or gp → update = gpupdate) 67 return validated, gpupdate, pupdate 68 } 69 Find(Key k): Leaf* { 70 Internal * gp, p 71 Leaf *l 72 Boolean validated 73 while True { 74 seq := Counter 75 −, p, l := Search(k, seq) 76 validated, −, − := ValidateLeaf(gp, p, l, k) 77 if validated then { 78 if l → key = k then return l 79 else return ⊥ 80 } 81} 82 } 83 CAS-Child(Internal *parent, Node *old, Node *new) { Precondition: parent points to an Int...

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Indexing metric uncertain data for range queries and range joins

Cited by 16 publications

References 43 publications

Uncertain Spatial Data Management:An Overview

Uncertain Spatial Data Management:An Overview

Overlay Indexes: Efficiently Supporting Aggregate Range Queries and Authenticated Data Structures in Off-the-Shelf Databases

Persistent Non-Blocking Binary Search Trees Supporting Wait-Free Range Queries

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