CN102880629A - Accelerating query method of probabilistic database - Google Patents
Accelerating query method of probabilistic database Download PDFInfo
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- CN102880629A CN102880629A CN2012102092704A CN201210209270A CN102880629A CN 102880629 A CN102880629 A CN 102880629A CN 2012102092704 A CN2012102092704 A CN 2012102092704A CN 201210209270 A CN201210209270 A CN 201210209270A CN 102880629 A CN102880629 A CN 102880629A
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Abstract
The invention provides an accelerating query method for a probabilistic database, which comprises the following steps: obtaining a probabilistic query keyword, wherein the probabilistic query keyword comprises a joint value of a plurality of variables; according to the probabilistic query keyword, querying in a cache; storing an intermediate operation result in the previous probabilistic query process into the cache; and if the intermediate operation result matched with the joint value of the variables is in the presence in the cache, taking the intermediate operation result as the query result of the probabilistic query. In a mode of storing the intermediate operation result in the cache, whether the matched intermediate operation result is in the presence in the cache is firstly queried when a new probabilistic query is made for the probabilistic database, and if so, the intermediate operation result is directly used as the query result of the probabilistic query so as to optimize the query speed of the probabilistic database.
Description
Technical field
The present invention relates to database technology, relate in particular to a kind of probability database speedup query method.
Background technology
Probability database is used for the storage uncertain data, and the inquiry that probability database is carried out is called probabilistic query.The value of each variable of probability database is not what determine, and variable is also existed the restriction of different conditions, and each condition also is uncertain.For example be arranged on temperature, humidity, air pressure and the intensity of illumination in a plurality of wireless senser Real-time Obtainings external world in same place, because physical equipment improves and limited electric power supply and network delay not, temperature, humidity, air pressure and the intensity of illumination in the same place of synchronization that each wireless senser obtains may be different.Information according to each wireless senser collection, can make up a probability database, the variable of this probability database is temperature, humidity, air pressure and intensity of illumination, preserves a plurality of values and and the probability of each value of each variable of the same place of synchronization in the probability database.Owing to may have association between each variable, so also preserve the joint distribution of associated variable in the probability database, be i.e. the probability of related a plurality of variable associating values.For instance, temperature exists related with humidity, temperature exists related with intensity of illumination, humidity exists related with air pressure, when probabilistic query relates to the related value of intensity of illumination and air pressure, then need to come according to temperature and the joint distribution of joint distribution, humidity and the air pressure of joint distribution, temperature and the intensity of illumination of humidity the probability of the related value of computing illumination intensity and air pressure.
Can find out that probabilistic query need to can obtain Query Result through very complicated matrix operation usually, cause the speed of probabilistic query very slow.
Summary of the invention
The embodiment of the invention provides a kind of probability database speedup query method, to improve the inquiry velocity to probability database.
The embodiment of the invention provides a kind of probability database speedup query method, comprising:
The acquisition probability key word of the inquiry, described probabilistic query key word comprises the associating value of a plurality of variablees;
Inquire about in buffer memory according to described probabilistic query key word, the intermediate operations result of probabilistic query process before preserving in the described buffer memory, if have the intermediate operations result of mating with the associating value of described a plurality of variablees in the described buffer memory, then with the Query Result of described intermediate operations result as described probabilistic query.
The method that the embodiment of the invention provides, mode by storage intermediate operations result in buffer memory, when probability database is done new probabilistic query, the intermediate operations result who at first whether has coupling in the query caching, if exist then directly with the Query Result of this intermediate operations result as this probabilistic query, thereby optimized inquiry velocity to probability database.
Description of drawings
Fig. 1 is probability database speedup query method the first embodiment process flow diagram of the present invention;
Fig. 2 is probability database speedup query method the second embodiment process flow diagram of the present invention;
Fig. 3 is the probability database structural drawing that utilizes Bayesian network to represent;
Fig. 4 is the associating tree structure diagram that probability database among Fig. 3 is made up;
Fig. 5 is for setting the partition structure figure that carries out after recurrence is divided to uniting among Fig. 4;
The tree index structural drawing of the probability database that Fig. 6 sets up for the partition structure figure that unites tree in Fig. 5;
Fig. 7 is the processing flow chart of intermediate operations result in the buffer memory.
Embodiment
Fig. 1 is probability database speedup query method the first embodiment process flow diagram of the present invention, as shown in Figure 1, the probability database speedup query method that the embodiment of the invention provides can be carried out by the Database Modeling instrument, and this method can adopt the form of software to realize that the method comprises:
Step S100, the acquisition probability key word of the inquiry, described probabilistic query key word comprises the associating value of a plurality of variablees;
Probability database is done probabilistic query, stored the marginal distribution of random occurrence in the probability database, and the joint distribution of a plurality of related random occurrences.Wherein, random occurrence refers to that variable gets the probability that a certain fixedly value and this variable equal this value.Marginal distribution refers to the value of this variable and the probability matrix that associated condition forms, and joint distribution refers to the probability matrix that a plurality of different values that have each other a variable of incidence relation form.The acquisition probability key word of the inquiry, wherein the probabilistic query key word comprises the associating value of a plurality of variablees.Preferably, the probabilistic query key word can be query formulation, has for example stored respectively marginal distribution, B marginal distribution, the C marginal distribution of A in the probability database, and the joint distribution of A, B, C, the probabilistic query key word is Q(A, B), i.e. the associating value of A and B is obtained in inquiry.
Step S102, inquire about in buffer memory according to described probabilistic query key word, the intermediate operations result of probabilistic query process before preserving in the described buffer memory, if have the intermediate operations result of mating with the associating value of described a plurality of variablees in the described buffer memory, then with the Query Result of described intermediate operations result as described probabilistic query.
When probability database is done probabilistic query, at first whether exist in the query caching with the probabilistic query key word in the intermediate operations result that is complementary of the associating value of a plurality of variablees, if the intermediate operations result that the associating value of existence and a plurality of variablees is complementary is then with the Query Result of this intermediate operations result as this probabilistic query.
The technical scheme that present embodiment provides is by the mode of storage intermediate operations result in buffer memory, when probability database is done new probabilistic query, the intermediate operations result who at first whether has coupling in the query caching, if exist then directly with the Query Result of this intermediate operations result as this probabilistic query, thereby optimized inquiry velocity to probability database.
Fig. 2 is probability database speedup query method the second embodiment process flow diagram of the present invention, and as shown in Figure 2, the method comprises:
Step S201 sets up the tree index corresponding with described probability database;
Step S202, the acquisition probability key word of the inquiry, described probabilistic query key word comprises the associating value of a plurality of variablees;
Step S203 inquires about in buffer memory according to described probabilistic query key word, judges the intermediate operations result who whether exists in the buffer memory with the associating value of a plurality of variablees coupling, if execution in step S204 then, if not execution in step S205 then;
Step S204, with the Query Result of described intermediate operations result as described probabilistic query, flow process finishes.
Step S205 decomposes described probabilistic query key word according to described tree index, obtains a plurality of sub-key words;
Step S206 inquires about respectively in buffer memory according to described a plurality of sub-key words, judges the intermediate operations result who whether exists in the described buffer memory with described each sub-keyword match, if execution in step S207 then, if not execution in step S208 then;
Step S207, with the intermediate operations result of described and described each sub-keyword match respectively as the Query Result of each sub-key word, execution in step S209;
Step S208, according to described at least one sub-key word with described tree index in inquire about;
If there is not the intermediate operations result with at least one sub-keyword match in the described buffer memory, then according at least one sub-key word with described tree index in inquire about;
Step S209 according to the Query Result of described each sub-key word, calculates the Query Result of described probabilistic query.
In preferred embodiment, by the mode of uniting tree probability database is set up index, concrete grammar is as follows:
Fig. 3 is the probability database structural drawing that utilizes Bayesian network to represent, as shown in Figure 3, comprise random occurrence X1 ~ X12 in this probability database, wherein X1 ~ X2 represents that the value of variables A is x1 or x2, X3 ~ X5 represents that the value of variable B is x3 or x4 or x5, X6 ~ X7 represents that the value of variable C is x6 or x7, X8 ~ X9 represents that the value of variables D is x8 or x9, X10 ~ X11 represents that the value of variable E is x10 or x11, X12 represents that the value of variable F is x12, R1 ~ R5 and G1 ~ G6 represent respectively the qualifications to random occurrence, this qualifications is so that exist certain incidence relation between the different random event, for example the X value is that x1 or value are between the x2 under the G1 condition, and at the incidence relation that has mutual exclusion between X value x1 and the x2 under the G1 condition, namely X can not the while value be x1 and x2.
Fig. 4 is the associating tree structure diagram that probability database among Fig. 3 is made up, and as shown in Figure 4, wherein border circular areas represents the association node of this index, and the rectangular area represents the partition node of this index.By uniting tree (junction tree) probability database is set up index.Wherein association node is the marginal distribution of random occurrence, i.e. the probability matrix that condition and random occurrence forms, and partition node is the probability matrix of the condition of related random occurrence and random occurrence.
Fig. 5 is for setting the partition structure figure that carries out after recurrence is divided to uniting among Fig. 4, the tree index structural drawing of the probability database that Fig. 6 sets up for the partition structure figure that unites tree in Fig. 5, as shown in Figure 5 and Figure 6, according to the associating tree construction of probability database, probability database is set up tree index.At first be divided into different unit to uniting the size of setting according to association node and partition node, the joint distribution of all partition node around all association nodes in this unit is stored in each unit.Further carry out secondary according to the size of unit and divide, set up the index structure of probability database, comprise root node, father node and child node in this index structure.Preferably, to uniting tree when dividing, each size of node is no more than 4K.Wherein root node comprises the sign of associating tree.Father node under the root node comprise all partition node around all association nodes joint distribution, with and child node in the sign of random occurrence.Child node under the father node comprises the marginal distribution of random occurrence, and the partition node of random occurrence corresponding to related this child node.
Particularly, the user can record the query note to probability database in the inquiry log of system when probability database is operated, comprise the information such as customer identification number, query time, query contents, Query Result in the query note.According to the inquiry log of probability database, can obtain the intermediate operations result that the user produces in query script, with this intermediate operations result store in buffer memory.The acquisition probability key word of the inquiry, the associating value that comprises a plurality of variablees in this probabilistic query key word, according to described probabilistic query keyword search buffer memory, if have the intermediate operations result of mating with the associating value of a plurality of variablees, then directly with the Query Result of this intermediate operations result as this probabilistic query.
If there is not the intermediate operations result who is complementary, then travel through the tree index of this probability database, according to tree index the probabilistic query key word is decomposed, obtain a plurality of sub-key words.One by one a plurality of sub-key words are inquired about respectively in buffer memory, are judged the intermediate operations result who whether exists in the buffer memory with each sub-keyword match, if then will with the intermediate operations result of each sub-keyword match respectively as the Query Result of each sub-key word.If there is not the intermediate operations result who is complementary in certain sub-key word in buffer memory, then this sub-key word is inquired about in tree index, obtain the Query Result of this sub-key word, and this Query Result is saved in the buffer memory as the intermediate operations result.At last, according to the Query Result of each sub-key word, calculate the Query Result of probabilistic query.
Owing in the middle of probability database, be not separate between each variable, but have different incidence relations.Therefore, by setting up index at probability database, and with after the intermediate operations result store is in buffer memory, as user again during query caching, when certain the intermediate operations result in this probabilistic query key word or sub-key word and the buffer memory is identical, could directly obtain this intermediate operations result as the Query Result of this probabilistic query.
When probabilistic query key word or sub-key word and intermediate operations result are incomplete same, then can not directly obtain the Query Result of this probabilistic query.For example, the intermediate operations of having stored random occurrence X2 and random occurrence X3 in the buffer memory is as a result the time, and wherein the probability of random occurrence X2 Query Result is P (X2), and the probability of random occurrence X3 Query Result is P (X3).The user makes new probabilistic query to probability database, need to obtain Q(X2, X3) value result, because have incidence relation between X2 and the X3, therefore the Query Result of (X2, X3) can not be directly by P (X2), P (X3) directly obtains P (X2, X3).Even therefore stored P (X2) and P (X3) in the intermediate database all at buffer memory, can not directly calculate P (X2, X3).
The index of traversal probability database according to the incidence relation between the variable in the probability database, by introducing the mode of bridging amount, is a plurality of sub-key words with a probabilistic query key word split, then according to sub-keyword search buffer memory.
Sub-key word refers to, the probabilistic query key word split is a plurality of sub-key words that are mutually related, and can access the Query Result of former probabilistic query key word according to the Query Result of all sub-key words.The bridging amount refers to the sub-key word of difference to be connected to the condition of probabilistic query key word.
For example former probabilistic query key word is Q(X2, X3) in buffer memory, there is not an intermediate operations result who is complementary with it, by introducing relevant bridging amount R1 all with random occurrence X2 and random occurrence X3, be a plurality of sub-key words with former probabilistic query key word split.The bridging amount refers to can connect the condition of random occurrence X2 and random occurrence X3 in inquiry.R1 is the bridging amount between random occurrence X2 and the random occurrence X3, then with former probabilistic query key word Q(X2, X3) split into Q(X2, R1) and Q(R1, X3).
With former probabilistic query key word Q(X2, X3) split into Q(X2, R1) and Q(R1, X3) afterwards, according to Q(X2, R1) and Q(R1, X3) query caching, if there is the intermediate operations result be complementary with it in the buffer memory, then with the Query Result of this intermediate operations result as sub-key word, then do not continue to search tree index if do not exist, obtain the Query Result of all sub-key words after, according to formula:
(X, Y are random occurrence, and S is the bridging amount) obtains the Query Result of probabilistic query.
Again for example, the probabilistic query key word is Q (X1, X12), and traversal index as can be known X1 and X12 is distributed among child node I1 and the child node I3, and child node I1 be connected child node I2 connection with child node I3.Therefore be a plurality of sub-key words: Q (X1, G2) with this query decomposition, Q (G2, G4), Q (G4, X12), wherein G2 and G4 are the bridging amount.Suppose directly to inquire about the index of probability database, owing to Q (G2, G4) can directly be obtained by index node I2, so do not need to be decomposed into again sub-key word not by intermediate database.
And Q (X1, G2) and Q (G4, X12) also need to continue to decompose, and Q (X1, G2) is decomposed into sub-key word Q (X1, R1) and Q (R1, G2); Q (G4, X12) is decomposed into sub-key word Q (G4, G5) and Q (G5, X12); Q (X1, R1) need to continue to be decomposed into sub-key word Q (X1, G1) and Q (G1, R1); Q (G5, X12) need to continue to be decomposed into sub-key word Q (G5, R6) and Q (R6, X12).
Therefore when calculating the probability of occurrence P (X1, X12) of Q (X1, X12), need to do altogether the inquiry of 11 second son key words.
Suppose in repeatedly inquiry before, in the intermediate database with P (X1, G2), P (G4, X12) deposit buffer memory in and suffered, we just only need original Q (X1, X12) is decomposed into sub-key word Q (X1 so, G2), Q (G2, G4), Q (G4, X12), and Q (G2, G4) can be directly obtains from the child node I2 of index, therefore directly obtains Q (X1, G2), the Query Result of Q (G4, X12) only needs the inquiry of three second son key words namely can obtain Q (X1, X12) probability of occurrence P (X1, X12).
After obtaining the Query Result of all sub-keyword messages, Query Result according to the probability distribution calculating probability key word of the inquiry of the Query Result of all sub-key words, wherein Query Result is the joint distribution of a plurality of variablees in the probabilistic query key word, and specific formula for calculation is:
P(X wherein) being the joint distribution of a plurality of variablees in the probabilistic query key word, P(C) is the joint distribution of a plurality of variablees in the sub-key word, P(S) is the marginal distribution of bridging amount.
In the technical scheme that present embodiment provides, by probability database being set up the mode of tree index, when making probabilistic query, according to probabilistic query keyword query buffer memory, if have the intermediate operations result who is complementary in the buffer memory, then directly obtain this intermediate operations result as the Query Result of probabilistic query key word; If there is not then the index according to probability database, by introducing the mode of bridging amount, be a plurality of sub-key words with the probabilistic query key word split, and again search whether be present in the intermediate operations result that sub-key word is complementary in the buffer memory, then directly obtain this intermediate operations result as the Query Result of sub-key word if exist.Mode by storage intermediate operations result in the buffer memory, reduced the computing of the probabilistic query that repeats, accelerated the speed of probabilistic query, simultaneously by setting up the mode of tree index, introducing the bridging amount is a plurality of sub-key words with the probabilistic query key word split, also greatly accelerate the inquiry to probability database, further accelerated the speed of probabilistic query.
Further, Fig. 7 is the processing flow chart of intermediate operations result in the buffer memory, and as shown in Figure 7, the method comprises:
Step S300 obtains the Query Result of sub-key word;
Step S302 judges whether the Query Result of sub-key word has existed identical intermediate operations result in buffer memory, if execution in step S304 then, if not execution in step S306 then;
Step S304 upgrades the described intermediate operations result's who records in the buffer memory holding time, and increases described intermediate operations result's hit-count;
Step S306 is saved in described intermediate operations result in the described buffer memory, record holding time, and the described intermediate operations result's of initialization hit-count;
Step S308 is according to holding time and the hit-count of each intermediate operations result in the described buffer memory, regularly or periodically each intermediate operations result is carried out burin-in process.
Particularly, in the process of probabilistic query, be a plurality of sub-key words to the probabilistic query key word split, obtain the Query Result of a plurality of sub-key words.Whether the Query Result of judging sub-key word has existed identical intermediate operations result in buffer memory.Preserved intermediate operations result, intermediate operations holding time of last time as a result in the buffer memory, and hit-count.If exist identical intermediate operations result then this intermediate operations result's holding time to be upgraded for this reason sub-keyword query result's the acquisition time, and this intermediate operations result's hit-count added one.
Because meeting produces a large amount of intermediate operations results in the probabilistic query process, therefore need regularly upgrade the intermediate operations result in the buffer memory, delete the intermediate operations result that hit-count is low or for a long time be not hit.Therefore according to holding time and the hit-count of each intermediate operations result in the buffer memory, regularly or periodically each intermediate operations result is carried out burin-in process.
One of ordinary skill in the art will appreciate that: all or part of step that realizes said method embodiment can be finished by the relevant hardware of programmed instruction, aforesaid program can be stored in the computer read/write memory medium, this program is carried out the step that comprises said method embodiment when carrying out; And aforesaid storage medium comprises: the various media that can be program code stored such as ROM, RAM, magnetic disc or CD.
It should be noted that at last: above each embodiment is not intended to limit only in order to technical scheme of the present invention to be described; Although with reference to aforementioned each embodiment the present invention is had been described in detail, those of ordinary skill in the art is to be understood that: it still can be made amendment to the technical scheme that aforementioned each embodiment puts down in writing, and perhaps some or all of technical characterictic wherein is equal to replacement; And these modifications or replacement do not make the essence of appropriate technical solution break away from the scope of various embodiments of the present invention technical scheme.
Claims (9)
1. a probability database speedup query method is characterized in that, comprising:
The acquisition probability key word of the inquiry, described probabilistic query key word comprises the associating value of a plurality of variablees;
Inquire about in buffer memory according to described probabilistic query key word, the intermediate operations result of probabilistic query process before preserving in the described buffer memory, if have the intermediate operations result of mating with the associating value of described a plurality of variablees in the described buffer memory, then with the Query Result of described intermediate operations result as described probabilistic query.
2. method according to claim 1 is characterized in that, described search in buffer memory according to described probabilistic query key word after, also comprise:
If do not exist in the described buffer memory, the intermediate operations result with the associating value of described a plurality of variablees is mated then inquires about probability database according to described probabilistic query key word.
3. method according to claim 2 is characterized in that, before the described acquisition probability key word of the inquiry, also comprises:
Set up the tree index corresponding with described probability database;
Describedly according to described probabilistic query key word probability database is inquired about, being comprised:
Inquire about in the tree index corresponding with described probability database according to described probabilistic query key word.
4. method according to claim 3 is characterized in that, the tree index that described foundation is corresponding with described probability database comprises:
Set up the unite tree corresponding with described probability database;
According to the described tree foundation tree index corresponding with described probability database of uniting.
5. method according to claim 3 is characterized in that, describedly inquires about in the tree index corresponding with described probability database according to described probabilistic query key word, comprising:
According to described tree index described probabilistic query key word is decomposed, obtain a plurality of sub-key words;
Inquire about respectively in buffer memory according to described a plurality of sub-key words, if there is the intermediate operations result with described each sub-keyword match in the described buffer memory, then with the intermediate operations result of described and described each sub-keyword match respectively as the Query Result of each sub-key word;
According to the Query Result of described each sub-key word, calculate the Query Result of described probabilistic query.
6. method according to claim 5 is characterized in that, described inquire about respectively in buffer memory according to described a plurality of sub-key words after, also comprise:
If there is not the intermediate operations result with at least one sub-keyword match in the described buffer memory, then according to described at least one sub-key word with described tree index in inquire about.
7. each described method is characterized in that according to claim 1-6, also comprises:
The Query Result of described sub-key word is saved in the described buffer memory as the intermediate operations result.
8. method according to claim 7 is characterized in that, also comprises:
When being saved in the intermediate operations result in the described buffer memory, judge and whether preserved described intermediate operations result in the described buffer memory, if then upgrade the described intermediate operations result's who records in the buffer memory holding time, and increase described intermediate operations result's hit-count, then described intermediate operations result is saved in the described buffer memory if not, record holding time, and the described intermediate operations result's of initialization hit-count.
9. method according to claim 8 is characterized in that, also comprises:
According to holding time and the hit-count of each intermediate operations result in the described buffer memory, regularly or periodically each intermediate operations result is carried out burin-in process.
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