CN105005584A - Multi-subspace Skyline query computation method - Google Patents

Multi-subspace Skyline query computation method Download PDF

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Publication number
CN105005584A
CN105005584A CN201510344545.9A CN201510344545A CN105005584A CN 105005584 A CN105005584 A CN 105005584A CN 201510344545 A CN201510344545 A CN 201510344545A CN 105005584 A CN105005584 A CN 105005584A
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subspace
skyline
data point
algorithm
point
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秦小麟
王潇逸
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Nanjing University of Aeronautics and Astronautics
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Nanjing University of Aeronautics and Astronautics
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Priority to CN201510344545.9A priority Critical patent/CN105005584A/en
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/903—Querying
    • G06F16/90335—Query processing

Abstract

The invention provides a method for computing multi-subspace Skyline queries and belongs to the orientation of data management and queries in the computer field. According to the method, firstly, in case of co-existence of multiple subspace Skyline queries in a database system, a sub-space cubic group structure is designed, and on the basis of the structure, a computation method capable of simultaneously processing multiple subspace Skyline queries, namely an MSSCA algorithm, is designed; the algorithm is capable of effectively solving the multi-subspace Skyline query problem. In the implementation process of the algorithm, a method of sharing Skyline result sets of child spaces is utilized thoroughly to directly put points that must be Skyline results of a parent space into the result set, and therefore, the times of judgment is reduced; besides, the algorithm is also capable of further reducing the times of dominance relation judgment by use of such methods as maximum value pruning and summating filtering; as a result, the efficiency is effectively improved. The method disclosed by the invention is capable of dealing with co-existence of multiple subspace Skyline queries in the database system and guaranteeing the efficiency of the algorithm by use of a series of sharing and filtering methods; in short, the method has a great practical application value.

Description

A kind of many subspace Skylines query count method
Technical field
The invention discloses a kind of many subspace Skylines query count method, being specifically related to a kind of query count method for calculating the Skyline inquiry on simultaneous in Database Systems, several subspaces, belonging to data management and inquiring technology field in computer realm.
Background technology
In recent years, Skyline calculating and computing method thereof obtain the concern of numerous researcher.Main such as, because Skyline Query Result has very important effect, multiobjectives decision in many applications, data mining and visual, and user preference inquiry etc.Database field initial Skyline inquiry research mainly concentrate on the total space Skyline inquiry, and along with the data in database be the trend development of higher-dimension magnanimity, in the total space calculated by very big Skyline result become meaningless.In many scenes, user is not necessarily interested in all dimensions of data set, is probably only concerned about certain the several dimension in whole dimension.Therefore, there is the concept of subspace Skyline, such as, the attributes such as flight information database comprises price, the departure time, lasts, intermediate stop, certain user will search a flight from Beijing to Nanjing, he requires low price and lasts shorter, then the Skyline inquiry that he sends is exactly the Skyline inquiry in subspace (price and last).
Traditional subspace Skyline algorithm mainly concentrates on the inquiry of a certain particular subspace and the inquiry to whole subspace.And in actual applications, user often has the demand from multi-angle close examination data set.Therefore, in most cases user the Skyline result of a certain particular subspace of non-interesting, meanwhile, the Skyline result on whole subspace there is no too large meaning to Most users.User can pay close attention to the combination of different subspace usually according to the point of interest of self, mainly may be summarized to be following two kinds of situations:
1. a certain user pays close attention to the combination of multiple different dimensions of a data set simultaneously, and such as, the data set of footballer comprises: (speed, physical efficiency, shooting precision, grab, strength) five dimensions.Football coach is in order to grasp the situation of sportsman, and for forward, coach may pay close attention to the combination of speed and shooting precision two dimensions, then may be concerned about the combination of his grabbing and strength two dimensions for rear guard coach;
2. many users are concerned about the various combination of a data set respectively, and such as, restaurant's data set comprises: (distance, price, attitude, dining room environment, dish code, taste) six dimensions.A few individual makes an appointment and together has a dinner party, and first is concerned about the position in restaurant and the combination of price two dimensions, and second is concerned about the size of dish and the combination of taste two dimensions, and third is concerned about the attitude of waiter and the combination of dining room environment two dimensions.
The problems referred to above may be summarized to be: for a data set, there is the subspace Skyline inquiry of multiple different dimensions combination in system simultaneously.Be referred to as many subspace Skylines inquiry problem, the algorithm of current relevant subspace Skyline is all concentrate on a certain particular subspace or whole subspaces mostly, therefore when solving any number of subspace Skyline inquiry problem under efficiency.Patent of the present invention pays close attention to the Skyline inquiry simultaneously calculated how efficiently on multiple subspace, provides a kind of algorithm calculating the inquiry of many subspace Skylines.Mainly be summarized as follows: first, based on the definition of Skycube, propose subspace cube group (Subspace Skycube Group, SSG) concept, and design the method being gathered generated subspace cube group structure by subspace.Afterwards, devise subspace Candidate Set set, make full use of the shared relationship of each subspace Skyline result in the cube structure of subspace to improve the efficiency of many subspace Skylines query processing.Based on subspace cube group structure, devise the method for the many subspace Skyline inquiries of a kind of Efficient Solution.
Summary of the invention
The user that the present invention exists from real world applications examines the problem of a data set closely from multi-angle, and then find in a system, for a data set S, often there is the demand of multiple subspace Skyline inquiry simultaneously, and be referred to as many subspace Skylines inquiry problem.In order to overcome the deficiency of traditional Skyline query count method when solving this problem, patent of the present invention provides a kind of many subspace Skylines query count method based on subspace cube group structure, the method can calculate simultaneous multiple subspace Skyline inquiry in Database Systems efficiently, meets the query demand of user.
Patent of the present invention adopts following technical scheme for solving its technical matters:
A kind of many subspace Skylines query count method, is characterized in that, comprise the following steps:
The multiple subspace Skyline inquiries existed in step one, first Test database system, and be integrated into set O, afterwards this subspace set O is configured to subspace cube group structure, for subsequent calculations.
Step 2, pre-service is carried out to the data set in Database Systems, in each dimension, data set is sorted respectively, form the data sequence that several are new.
In the process of step 3, calculating subspace cube group structure, to every sub spaces generated subspace Candidate Set, by this Candidate Set, directly eliminate the point that is decided to be subspace Skyline result, improve efficiency of algorithm.
Step 4, employing summation filter method carry out cutting and filtration to the data set in Database Systems, and the point that directly will belong to subspace Skyline result is scarcely got rid of.
Step 5, employing maximal value method of cutting out, cannot arrange the whole cutting of data of other data points, avoid unnecessary dominance relation to judge.
As described in step one, all subspaces existed in detection system, form set O, using O as input, through createSSG algorithm, by all subspaces in set O, are configured to subspace cube group SSG.First, SSG is initialized as sky, then for every sub spaces, first judge whether belong to SSG with its subspace cube (SSC) as summit, if do not belonged to, then SSC is set up to this subspace, and add SSG, otherwise, process next subspace.
As described in step 2, pre-service is carried out to data set, by data set respectively at the enterprising line ordering of all dimensions that subspace cube group ground floor comprises, thus forms several data sequences.
As described in step 3, current just at calculated subspace V for one, the computing method of its Candidate Set are: first ask also to V all child subspaces result set, getting rid of all child subspaces result afterwards concentrates on by the data point that other data point is arranged on V, and the set of the final data point formed is then.The Candidate Set of subspace V.This Candidate Set has following effect: first, achieve sharing of subspace Skyline result, ensure MSSCA algorithm the dependent all subspace Skyline results asked in the cube group of subspace, the solving of upper strata is based upon on the result basis that lower floor obtained; The second, for the calculating that each subspace Skyline is inquired about, reduce the input quantity of data point; 3rd, because the data point in Candidate Set necessarily belongs to the Skyline result of current calculating subspace V, therefore effectively avoid the domination deterministic process to this partial data point, decrease number of comparisons significantly.Therefore subspace Candidate Set, effectively raises efficiency of algorithm.
As described in step 4, calculating the Skyline result set sky of a certain subspace V vprocess in, the process that an inevitable dominance relation judges, the present invention adopts summation filter method to be optimized this process, and the time complexity that this filtercondition performs is O (1), if meet this condition, then need not carry out dominance relation judgement to data point p.Obviously, by the method, in the implementation of algorithm, time complexity is that the dominance relation decision process number of times of O (d) will greatly reduce.
This filtercondition be utilize the dimension values of data point on the V of subspace and carry out filtering, for a certain data point p, p at the design for filtration elements of subspace V be:
F V ( p ) = Σ ∀ a i ∈ V p ( a i )
Above-mentioned formula shows, the filter value of p point on V is F v(p), this value equal p point on V all dimension values and.By this filtrator, be easy to the dominance relation of preliminary judgement two data point p and q, if F v(p)≤F vq (), obviously shows that q spatially can not arrange p at V, because on V, q has at least the value in a dimension to be greater than p.
In the implementation of algorithm, two places utilize this filter value to raise the efficiency.First, the data point in computation process in all subspace Skyline result sets all sorts from small to large according to filter value; In addition, at the Skyline result sky calculating a certain subspace V vtime, resulting number strong point p and current sky vfiltercondition test is carried out, if F before the dominance relation of middle data point q v(p)≤F v(q), then obvious sky vmiddle data point q cannot arrange p, and due to sky vmiddle data point is by the sequence of filter value non-decreasing, so the point after q also cannot arrange p point, then p point is filtered, and directly puts into sky vin; If F v(p) > F vq (), just needs to carry out numeric ratio in dimension one by one comparatively to determine dominance relation to data point p and q.
As described in step 5, adopt maximal value method of cutting out, if namely the minimum value of data point p on the V of subspace is greater than current sky vin maximal value, then obviously data point p can by sky vin certain some domination, therefore these data points can be directly deleted, utilizes the dominance relation decision process that this method avoid most data point.
The present invention adopts above technical scheme compared with prior art, has following beneficial effect:
(1) user that the present invention exists from real world applications examines the problem of a data set closely from multi-angle, and then proposes many subspace Skylines inquiry problem.On this basis, the invention provides a kind of many subspace Skylines query count method based on subspace cube group structure, the method calculates simultaneous multiple subspace Skyline inquiry in Database Systems efficiently.
(2) the present invention proposes a kind of subspace cube group structure, effectively the multiple subspaces in system are organized, define the institutional framework be convenient to MSSCA algorithm and carried out calculating, and utilize the shared relationship of each subspace Skyline result in this structure to improve the efficiency of many subspace Skylines query processing.
(3) based on subspace cube group structure, the invention provides algorithm--the MSSCA algorithm of the many subspace Skyline inquiries of a kind of Efficient Solution.In the implementation process of algorithm, design and have employed subspace Candidate Set, summation filtration and maximal value and filter three kinds of beta pruning filter methods, effectively reduce data number of comparisons, improve efficiency.
Accompanying drawing explanation
Fig. 1 is one-piece construction figure of the present invention.
Fig. 2 is that subspace cube all living creatures becomes process schematic.
Fig. 3 is the subspace cube group structure exemplary plot of four-dimensional data set.
Fig. 4 is the process flow diagram of subspace candidate generation process.
Fig. 5 is many subspace Skylines query count method flow diagram.
Embodiment
In order to solve the problem of many subspace Skyline inquiries in Database Systems, the invention provides a kind of efficient many subspace Skylines search algorithm, its overall flow as shown in Figure 1.
Below in conjunction with drawings and Examples, technical scheme of the present invention is described in further details:
Embodiment one
Embodiments of the invention one describe the method by multiple subspace generated subspace cube group (SSG), and this structure ensure that effective execution of subsequent algorithm, and concrete steps flow process as shown in Figure 2, comprising:
(1) first detect all subspaces in Database Systems, form subspace set O={V 1..., V n, wherein, the subspace number comprised is assumed to be n;
(2) the conjunction SSG that trooped by subspace cube is initialized as empty set, next processes all subspaces in subspace set O one by one;
(3) if all subspaces are all processed, then current SSG is returned, terminate this process, otherwise continue;
(4) if work as the subspace V of pre-treatment ibelong to SSG, then process the next one, otherwise continue;
(5) by V iplace is configured to a sub spaces cube SSC, and is joined in SSG;
(6) the above-mentioned SSG calculated is returned as a result.
Such as, for four dimension data, dimension is respectively a, b, c, d.Assuming that there are 4 sub spaces in system simultaneously, be O={ab, abc, acd, a}.Process described by said method, algorithm is by O generated subspace cube group structure, and specifically as shown in Figure 3, this subspace cube group comprises two sub spaces cubes, and its red subspace of getting the bid only need calculate once.
Embodiment two
Embodiments of the invention two as shown in Figure 4, describe the generation method of subspace Candidate Set (SSKY), SSKY avoids a judgement data point being decided to be subspace Skyline result being carried out to dominance relation, and improve efficiency, specific implementation process comprises:
(1) first for the subspace V calculated, the subspace cube SSC corresponding to it, finds all child subspaces of V;
(2) union is asked to the Skyline result set of all child subspaces of V, and be assigned to SSKY;
(3) for the Skyline result of each child subspace of V, finding out is not wherein the data point of Skyline result on V, and is assigned to TEMP;
(4) from SSKY, the TEMP of child's subspace Skyline result set of all V is removed;
(5) last, the SSKY obtained is then the Candidate Set of subspace V.
Embodiment three
Embodiments of the invention three as shown in Figure 5, describe the concrete execution flow process of MSSCA algorithm in the present invention, based on describing above, embodiment shows the many subspace Skylines search algorithm in conjunction with multiple optimization method based on subspace cube group---MSSCA algorithm.Comprise:
First algorithm utilizes the subspace cube generation method in embodiment one, carries out pre-service, O={V is gathered in subspace to the multiple subspaces existed in system 1..., V vbe converted into subspace cube group; Next pre-service is carried out to data set, by data set respectively at the enterprising line ordering of all dimensions that subspace cube group ground floor comprises, thus form several data sequences; Afterwards, because all subspaces of ground floor are all 1 dimension, therefore the Skyline result of ground floor subspace directly can obtain according to corresponding ordered data sequence; The Skyline solution procedure of every sub spaces from the second layer afterwards, all be based upon on the basis of current calculated all subspace Skyline results, first utilize the subspace candidate generation method in embodiment two, generate the Candidate Set of current subspace, then choose a data sequence l aidata set is traveled through, generates the Skyline result of every sub spaces one by one.
When traveling through data sequence and generating the result set of the Skyline of every sub spaces, due to the most consuming time be also that to call operation the most frequently as judgement computing to data point dominance relation, therefore reduce as far as possible or avoid unnecessary dominance relation judgement computing to be the key point place of improving efficiency of algorithm, MSSCA method mainly have employed following three kinds of methods and carries out cutting to data point, judges to reduce unnecessary dominance relation:
1. maximal value cutting: first, adopts maximal value method of cutting out, if namely the minimum value of data point p on the V of subspace is greater than current sky vin maximal value, then obviously data point p can by sky vin certain some domination, therefore these data points can be directly deleted.
2. subspace Candidate Set filters: by known above, the data point in the Candidate Set SSKY of subspace must be the Skyline result of current subspace, and therefore, these data points directly can be added into sky vin.
3. summation filter method: according to the description of this chapter the 3rd trifle to summation filtrator, when the filter value of data point p is less than or equal to sky vin certain any filter value time, illustrate that p point can not by sky vmiddle any point arranged, and therefore such data point p mono-is decided to be the Skyline result of current subspace V.
Below by reference to the accompanying drawings and three embodiments embodiments of the present invention are explained in detail, but the present invention is not limited to above-mentioned embodiment, in the ken that those of ordinary skill in the art possess, can also make a variety of changes under the prerequisite not departing from present inventive concept.

Claims (5)

1. the query count of subspace Skyline a more than method, is characterized in that, comprise the following steps:
The multiple subspace Skyline inquiries existed in step one, first Test database system, and be integrated into set O, afterwards this subspace set O is configured to subspace cube group structure, for subsequent calculations.
Step 2, pre-service is carried out to the data set in Database Systems, in each dimension, data set is sorted respectively, form the data sequence that several are new.
In the process of step 3, calculating subspace cube group structure, to every sub spaces generated subspace Candidate Set, by this Candidate Set, directly eliminate the point that is decided to be subspace Skyline result, improve efficiency of algorithm.
Step 4, employing summation filter method carry out cutting and filtration to the data set in Database Systems, and the point that directly will belong to subspace Skyline result is scarcely got rid of.
Step 5, employing maximal value method of cutting out, cannot arrange the whole cutting of data of other data points, avoid unnecessary dominance relation to judge.
2. a kind of many subspace Skylines query count method as claimed in claim 1, when it is characterized in that building subspace cube group structure, comprising:
The all subspaces existed in detection system, form set O, using O as input, through createSSG algorithm, by all subspaces in set O, are configured to subspace cube group SSG.First, SSG is initialized as sky, then for every sub spaces, first judge whether belong to SSG with its subspace cube (SSC) as summit, if do not belonged to, then SSC is set up to this subspace, and add SSG, otherwise, process next subspace.
3. a kind of many subspace Skylines query count method as claimed in claim 1, is characterized in that the generative process of subspace Candidate Set, comprising:
Current just at calculated subspace V for one, the computing method of its Candidate Set are: first ask also to V all child subspaces result set, getting rid of all child subspaces result afterwards concentrates on by the data point that other data point is arranged on V, and the set of the final data point formed is then the Candidate Set of subspace V.This Candidate Set has following effect: first, achieve sharing of subspace Skyline result, ensure MSSCA algorithm the dependent all subspace Skyline results asked in the cube group of subspace, the solving of upper strata is based upon on the result basis that lower floor obtained; The second, for the calculating that each subspace Skyline is inquired about, reduce the input quantity of data point; 3rd, because the data point in Candidate Set necessarily belongs to the Skyline result of current calculating subspace V, therefore effectively avoid the domination deterministic process to this partial data point, decrease number of comparisons significantly.Therefore subspace Candidate Set, effectively raises efficiency of algorithm.
4. a kind of many subspace Skylines query count method as claimed in claim 1, is characterized in that the enforcement of summation filter method, comprising:
Calculating the Skyline result set sky of a certain subspace V vprocess in, the process that an inevitable dominance relation judges, the present invention adopts summation filter method to be optimized this process, and the time complexity that this filtercondition performs is O (1), if meet this condition, then need not carry out dominance relation judgement to data point p.Obviously, by the method, in the implementation of algorithm, time complexity is that the dominance relation decision process number of times of O (d) will greatly reduce.
This filtercondition be utilize the dimension values of data point on the V of subspace and carry out filtering, for a certain data point p, p at the design for filtration elements of subspace V be:
Above-mentioned formula shows, the filter value of p point on V is F v(p), this value equal p point on V all dimension values and.By this filtrator, be easy to the dominance relation of preliminary judgement two data point p and q, if F v(p)≤F vq (), obviously shows that q spatially can not arrange p at V, because on V, q has at least the value in a dimension to be greater than p.
In the implementation of algorithm, two places utilize this filter value to raise the efficiency.First, the data point in computation process in all subspace Skyline result sets all sorts from small to large according to filter value; In addition, at the Skyline result sky calculating a certain subspace V vtime, resulting number strong point p and current sky vfiltercondition test is carried out, if F before the dominance relation of middle data point q v(p)≤F v(q), then obvious sky vmiddle data point q cannot arrange p, and due to sky vmiddle data point is by the sequence of filter value non-decreasing, so the point after q also cannot arrange p point, then p point is filtered, and directly puts into sky vin; If F v(p) > F vq (), just needs to carry out numeric ratio in dimension one by one comparatively to determine dominance relation to data point p and q.
5. a kind of many subspace Skylines query count method as claimed in claim 1, is characterized in that the enforcement of maximal value method of cutting out, comprising:
Adopt maximal value method of cutting out, if namely the minimum value of data point p on the V of subspace is greater than current sky vin maximal value, then obviously data point p can by sky vin certain some domination, therefore these data points can be directly deleted, utilizes the dominance relation decision process that this method avoid most data point.
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CN106649489A (en) * 2016-09-28 2017-05-10 南京航空航天大学 Continuous skyline query processing mechanism in geographic text information data
CN107046557A (en) * 2016-12-14 2017-08-15 大连大学 The intelligent medical calling inquiry system that dynamic Skyline is inquired about under mobile cloud computing environment
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CN109635048A (en) * 2018-10-29 2019-04-16 南京航空航天大学 More mobile subscriber's dynamic skyline querying methods based on road network
CN110245151A (en) * 2019-05-30 2019-09-17 湖南大学 Group of data points querying method, device, computer equipment and storage medium
CN113656447A (en) * 2021-09-01 2021-11-16 燕山大学 Skyline-like query method in three-dimensional obstacle space

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Cited By (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105608206A (en) * 2015-12-25 2016-05-25 天津理工大学 Data-broadcasting-oriented location correlation skyline query processing method
CN106649489A (en) * 2016-09-28 2017-05-10 南京航空航天大学 Continuous skyline query processing mechanism in geographic text information data
CN106649489B (en) * 2016-09-28 2020-06-09 南京航空航天大学 Continuous skyline query processing mechanism in geographic text information data
CN107046557A (en) * 2016-12-14 2017-08-15 大连大学 The intelligent medical calling inquiry system that dynamic Skyline is inquired about under mobile cloud computing environment
CN107967431A (en) * 2017-12-20 2018-04-27 南京航空航天大学 A kind of secret protection skyline querying methods on vertical distribution data set
CN109635048A (en) * 2018-10-29 2019-04-16 南京航空航天大学 More mobile subscriber's dynamic skyline querying methods based on road network
CN109635048B (en) * 2018-10-29 2021-03-09 南京航空航天大学 Multi-mobile-user dynamic skyline query method based on road network
CN110245151A (en) * 2019-05-30 2019-09-17 湖南大学 Group of data points querying method, device, computer equipment and storage medium
CN110245151B (en) * 2019-05-30 2021-07-13 湖南大学 Data point group query method and device, computer equipment and storage medium
CN113656447A (en) * 2021-09-01 2021-11-16 燕山大学 Skyline-like query method in three-dimensional obstacle space
CN113656447B (en) * 2021-09-01 2023-05-19 燕山大学 Skyline-like query method in three-dimensional obstacle space

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Application publication date: 20151028