JPH05225238A - Data base retrieval system - Google Patents

Abstract

PURPOSE:To increase the retrieval speed and to use the retrieval result again in the data base retrieval system using complicated conditional expressions. CONSTITUTION:Retrieval on prescribed conditions A, B, etc., is performed in a retrieval part 12, and retrieval results are stored in a retrieval result list storage part 14. In the case of retrieval on complicated conditions such as A or B, A and B, an object list condition retrieval part 16 uses the results of retrieval on conditions A and B, which are stored in the retrieval result list storage part 14, to perform the retrieval. Since the data base is not directly retrieved on complicated conditions, the retrieval time is shortened. Since results of retrieval on simpler conditions are used again, the retrieval efficiency is high.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a database search system, and more particularly to a database search system for retrieving necessary information from a database according to a plurality of conditions.

[0002]

2. Description of the Related Art When a condition search is performed in a database search by searching all properties, a predetermined search key is set, and this search key is applied to all properties to perform the search. For example, it is checked whether or not each property includes the search key, and the property including the search key is listed as the search result.

When performing a search using a conditional expression (search expression) consisting of a plurality of search keys in such a search, formulate a conditional expression with a plurality of search keys and perform a search using this. Has been done conventionally. For example, by keys A, B, C, D, (A or B or C) and
A conditional expression such as D is created, and all properties are searched using this expression.

[0004]

However, since such a search uses a conditional expression composed of a plurality of keys, the search time is very long and the cost performance when the condition is not satisfied is low. Also, similar conditional expressions, for example, similar to the above conditional expressions (A or B or C) and
When performing a search using a conditional expression such as E, there is a drawback that the efficiency is low because the partial logical condition (A or B or C) of the search already performed cannot be reused.

The present invention solves the above-mentioned conventional drawbacks, and in the search by a plurality of conditions, a high-speed condition search can be performed regardless of the complexity of the conditional expression, and the search results can be reused. The purpose is to provide a simple database search system.

[0006]

A database search system according to the present invention stores a search means for searching all properties under a predetermined condition, a search result storage means for storing search results by the search means, and a search result storage means. And a condition retrieval means for conducting a condition retrieval using the retrieved retrieval result.

[0007]

According to the present invention, when all properties are searched by a plurality of conditions, the search means searches by a predetermined condition among the plurality of conditions, and the result of the search is stored in the search result storage means. .. Next, using this search result, the condition search means searches for a complicated condition. Therefore, a search result based on a predetermined condition is stored, and a search for a more complicated condition is performed using this, so that a partial search result can be reused and a high-speed condition search can be performed.

[0008]

1 shows an embodiment of the system according to the invention. As shown in the figure, this system includes a search unit 12, a search result list storage unit 14, and a property list condition search unit 16. The search unit 12 is a search key input unit 18
This is a search unit that searches all properties for a search target 10 that is a database according to a conditional expression including a predetermined search key input from.

For example, when the keys input from the search key input unit 18 are A and B as shown in the figure, the search unit 12 searches by the conditional expressions A and B, respectively, and the result is the search result list. It is stored in the storage unit 14. In the example shown in the figure, the property number lists searched by the conditional expression A are 3, 5, 10, 20 and the property number lists searched by the conditional expression B are 5, 10, 30. These property number lists are stored in the search result list storage unit 14 as search results.

The property list condition search unit 16 is a search unit that uses the results stored in the search result list storage unit 14 to perform a search using a more complicated conditional expression. For example, using the above-mentioned search results by the conditional expressions A and B, the conditional expression (A or
B) or (A and B), the property list condition search unit 16 reads the search results by the conditional expressions A and B from the search result list storage unit 14 and uses them to search for the conditional expressions. (A or B) or (A
and search according to B).

In the case of the present embodiment, the result stored in the search result list storage unit 14 is the conditional expression A as described above.
The number list of properties searched by is 3, 5, 10,
20 and the number list of properties searched by the conditional expression B is 5, 10, 30. Therefore, the conditional expression (A or
In the case of the search of B), by obtaining the OR of these property number lists, the property number lists 3, 5, 10, 2
0 and 30 are obtained. Similarly, conditional expression (A and B)
In the case of the search, the property number lists 5 and 10 are obtained by calculating the AND of these property number lists.
The results of the obtained property number list are sent again to the search result list storage unit 14 and stored therein.

Therefore, by using these lists, the property list condition search unit 16 further (A or B or
A search using a conditional expression such as C) or (A or B or C) and E can be similarly performed.

According to this embodiment, the search result list storage unit 14 stores the result of the search by the conditional expression consisting of the predetermined search keys as described above, and the combination of these keys is complicated. When performing a search using a conditional expression, a conditional search is performed using the stored property number list.

Therefore, in the case of a search by a complicated conditional expression, it is not necessary to search for all properties by a complicated condition, so that the search time can be shortened. Further, since the result of the partial condition search is reused to perform the search, the search efficiency is good.

The retrieval system according to the present invention can be applied to retrieval of various databases. For example, it can be applied to the retrieval of conditional expressions in the following data retrieval system.

FIG. 2 is a data flow diagram of a pattern search system by the neighborhood feature quantity showing an embodiment to which the present invention is applied. In this search system, a neighborhood feature amount is created in which all phase information of events (information) is removed from all target properties in advance, and all properties are searched for the data group. The search algorithm includes a learning step and a search step. In the learning step, a neighborhood feature quantity matrix is created for each property. In the search step, a matching operation between the search key and the neighborhood feature amount matrix is performed to obtain an evaluation result indicating the matching degree (similarity) for each property. Each step will be described below.

(1) Learning Step In FIG. 2, the search target 10 is, for example, Japanese, English,
Document data in German, French, Hebrew, Russian, etc., or quantized waveform numerical data, chemical structural formulas, genetic information, and the like. For such a search target, the normalization means S1 first performs a normalization process. In general, a search target is represented by a column of minimum units of information (characters such as alphabets in the case of documents, real numerical values at a certain time in the case of numerical charts). It is converted into an integer sequence of n gradations by some method. This is called data normalization.

For example, in the case of English document data, ASCII
By using the code table as it is, the following 25
It is realized as a numerical expression with 6 gradations. …… This is a pen. …… 84 ｜ 104 ｜ 105 ｜ 115 ｜ 32 ｜ 105 ｜ 115 ｜ 32 ｜ 97 ｜ 32 ｜ 112 ｜ 101 ｜ 110 ｜ 46 ｜

In the above code, T is 84 and h is 10
Four . ． It corresponds to.

The normalized data 20 is then convoluted into the form of the neighborhood feature quantity matrix 30 by the learning means S2. Here, various arithmetic expressions for obtaining the neighborhood feature amount are possible. This arithmetic expression also affects the sharpness of search (the degree of overdetection is small).

[0021] Now, j-th data (characters) to C _i of the i-th property _(document), and _j, C _i, the quantization amount x and C _i relates _j, quantization amount for Upcoming k near the _j y Is calculated as follows. Here, the target property (document) to be searched is n
Given that there are individual pieces, the quantization of the i-th property will be described. Assuming that the normalized numerical value sequence 135,64,37,71,101, ... is arranged in the i-th property as shown in FIG. 3, the quantization amount x for C _{i, j} is x = f (C _{i, j} ) Quantization amount y for the front k neighborhood of C _{i, j} is y = g (C _{i, j} , C _{i, j + 1,} C _{i, j + 2, ...,} C _{i , j + k} ).

Here, f (C _{i, j} ) is an n-step quantization function for C _{i, j} . That is, it is a value obtained by performing a predetermined operation on the j-th data C _{i, j} of the i-th property, and is represented by any integer of 1 to n. Therefore, the position in the x-axis direction in the matrix (coordinates) shown in FIG. 4 is determined within the range of 1 to n by the obtained value of x.

Further, g (C _{i, j} , C _{i, j + 1,} C _{i, j + 2, ...,}
C _{i, j + k} ) is an m-step quantization function with respect to the front k neighborhood of C _{i, j} . That is, it is a value obtained by performing a predetermined operation on the j-th data C _{i, j of} the i-th property and a predetermined number of data in the vicinity of that data, and is represented by an integer of 1 to _m. It For example, as shown in FIG. 3, when the j-th data C _{i, j} is 135 and k is 3,
The data 64, 37, 71 following the data 135 are extracted as C _{i, j + 1,} C _{i, j + 2,} C _{i, j + 3} , and a predetermined calculation is performed on the correlation between these data and the data 135. . When the j-th data C _{i, j} is the next 64, C _{i, j + 1,} C _{i, j + 2,}
Data 37, 71, 1 following data 64 as C _{i, j + 3}
01 is extracted, and a predetermined calculation is performed on the correlation between these data and the data 64.

According to the value of y thus obtained,
The position in the y-axis direction in the matrix (coordinates) shown in FIG.
Determined in the range of m. Therefore, by determining x and y as described above, the position in the matrix (coordinates) shown in FIG. 4 is determined.

In the present system, each piece of property information has a serial number i and a weight w of the property for x and y obtained as described above.
It is stored as a set of (x, y, i). The weight w (x, y, i) is obtained from the data x, y, i by a predetermined calculation,
Normally, the value of the weight w (x, y, i) is fixed to 1.

The data C _{i, j} obtained as described above
The data is stored for each one based on the x, y values, as indicated by the bars in FIG. That is, the data in which the serial number i of the property and its weight w (x, y, i) are combined is stored at the position of the coordinates determined by the values of x and y of the data C _{i, j} . In the figure, the length of the bar is shown to be extended each time such data is stored. Usually weight w
Since (x, y, i) is set to 1, only the data of the serial number i of the property is stored at the position of the coordinates determined by the values of x and y.

The identification number of the property is added to the neighborhood feature amount matrix created in this way, and the structure file 40 is saved.

(2) Search Step First, the search key 50 is input. For example, "This is a pe
n. "is used as the search key. The key information is normalized to an integer sequence by the normalization means S3 based on the same normalization method as the learning step for this search key 50. 84 | 104 | 105 | 115 | 32 ｜ 105 ｜ 115 ｜ 32 ｜ 97 ｜ 32 ｜ 112 ｜ 101 ｜ 110 ｜ 46 ｜

Next, in the search means S4, a set of x and y from the head of the normalized numerical value sequence corresponding to each property using the same neighborhood feature extraction formulas f () and g () as in the learning step. Create a series of. Next, based on the series of the set of x and y, the search key content frequency ω _k for the property k is V
It is calculated by summing (x _j, y _j, k) for j = 1 to m.

However, V (x _j, y _j, k) is set to be equal to the weight when the property information list has the weight for the property i, and is set to 0 when the property information list does not have the weight.

Therefore, when there is data at the x, y position in FIG. 4 (when there is a bar) corresponding to the x, y pair of the numerical sequence to be searched (when there is a bar), that data in the storage means provided separately. The value of the weight is stored in the storage location of the serial number i of the property shown in FIG.

Next, in the evaluation result output means S5, the structural evaluation value score (degree of coincidence) obtained for each property is an evaluation value in the case of perfect match (in this case, the number of characters in the search key information-
Divide by k) to obtain the search key content probability, and obtain a list 70 of evaluation results. Further, the sorting means S6 sorts the list 70 in descending order of content probability to obtain a sorted list 80.

This sorted list 80 is a search result, and by referring to the higher-ranked property, it is possible to know the property name with a high probability that the search key is included in the property. Since the content probability is obtained for all of the perfect match and the incomplete match, the fuzzy match search can be performed.

Further, since the search is for all properties for all the information of the search key, the probability of missing the search is essentially zero.

Further, the evaluation time of the search key for one property depends only on the number of characters of the key and does not depend on the size of the property. Therefore, the search can be performed very quickly.

As a result of the data search as described above, the property whose score (key content probability) is higher than a predetermined threshold is extracted from the sorted list 80, and the extracted result is obtained by the conditional expression A or B. And The number list of these properties is stored in the search result list storage unit 14 of FIG. 1, and based on this, the property list condition search unit 16 performs the condition search as described above.

In the data search as described above, since the search result is obtained as the sorted list 80, this is stored in the search result list storage unit 14 and the conditional search is performed based on the stored data. Even in the case of complicated conditional expressions, the search can be performed at high speed.

The neighborhood feature extraction formula of the formula (1) can be variously considered in addition to the above example. For example, if f: x → xg: (x, y) → xy (or | xy |), the difference between adjacent characters and every other character (or the absolute value of the difference) is used as the neighborhood feature quantity. You can make a matrix. Alternatively, the neighborhood feature amount may be extracted by performing four arithmetic operations on individual character integer values of some character strings.

The neighborhood feature amount does not have to be extracted for all the data of each property. For example, a specific one or more integer values in property data, an integer value in a specific range,
Alternatively, the neighboring feature amount may be created (extracted) by excluding a specific one or one or more bits in each byte forming the data string. In the case of a double-byte character like a Japanese document, for example, the upper byte may be excluded and the lower-order byte may be taken as the target to extract the neighborhood feature amount.

In the above example, the neighborhood feature quantity matrix is 256
This is the next bit matrix, which corresponds to 8 Kbytes.
Therefore, it cannot be said that a database in which the data for one property is about 1 Kbyte is an efficient system.
Therefore, it is preferable to reduce the capacity of the structure file 40 by providing data compression means S7 to perform data compression.

FIG. 5 shows an example of the data compression method. In this example, the property name 40a (identification code) whose element value is 1 is stored as a 1-byte / case data string for each element of the 256th-order neighborhood feature amount matrix. Therefore, the property name whose element value is 0 is excluded as unnecessary data.

When the number of properties is 255 or more, the property name 40a cannot be represented by 1 byte, so only the lower 1 byte is stored. For example, when the number of properties is 10,000, the property name is represented by 2 bytes, but the lower 1 byte is used. Then, every time the property name code exceeds 255, the marker 40b is inserted into the data string.

At the time of search, the data string of the structure file corresponding to each of the neighborhood feature amounts of the search key is taken out, and the appearance frequency table for each property name is created. At this time, the marker 40
Add 255 to the property name code every time it exceeds b. The evaluation result list 70 of FIG. 2 is obtained based on the appearance frequency table created in this way.

If the data string of the property name code is, for example, more than half of all properties, the neighboring feature amount matrix element may be regarded as common for each property and the element may be deleted.

In the above embodiment, the normalizing means S1,
Learning means S2, normalization means S3, search means S4, evaluation result output means S5, sorting means S6, data compression means S7.
Can be configured by a computer program, but dedicated hardware may be configured by using a logic circuit element.

[0046]

According to the system of the present invention, since the result of the search under the predetermined condition is stored, the search under the complicated condition can be performed at high speed. Further, since partial search results can be reused, there is no waste and the search efficiency is high.

[Brief description of drawings]

FIG. 1 is a data flow diagram of an embodiment of a database search system according to the present invention.

FIG. 2 is a data flow diagram of a database search system to which the search system according to the present invention is applied.

FIG. 3 is a diagram showing quantization of neighborhood information.

FIG. 4 is a diagram showing a stored information structure.

FIG. 5 is a data configuration diagram of a compressed neighborhood feature amount.

[Explanation of symbols]

 10 search target 12 search unit 14 search result list storage unit 16 property list condition search unit 18 search key input unit 20 normalized data 30 neighborhood feature amount matrix 40 structure file 50 search key 60 normalization key 70 evaluation result list 80 sorted list S1 normalization means S2 learning means S3 normalization means S4 search means S5 evaluation result output means S6 sorting means S7 data compression means

Claims (4)

[Claims] 1. A search means for searching all properties according to predetermined conditions, a search result storage means for storing search results by the search means, and a condition search using the search results stored in the search result storage means. A condition search means for performing the search, wherein the condition search means performs a search based on a combination of the conditions used in the search by the search means,
A database search system, characterized in that the search is performed based on the search results stored in the search result storage means. 2. The database search system according to claim 1, wherein the system further comprises input means for inputting the condition. 3. A storage unit that stores the neighborhood feature amount for each search target property, and the degree of matching between the search key neighborhood feature amount and the search target neighborhood feature amount is determined for each property, and the property number is matched. The database search system according to claim 1, wherein the database search system comprises a search means for outputting in descending order of degree. 4. The quantization amount x for the j-th data string C _{i, j} of the i-th property to be searched and k data strings C _{i, j + 1,} C _{i, j + 2, in the} vicinity thereof _{. ..,} C _{i, j + k} quantized amount y and x = f (C _{i, j} ) y = g (C _{i, j} , C _{i, j + 1,} C _{i, j + 2, ..,} C _{i, j + k} ), and is used for a database search for storing the serial number i of the property at the position of the storage means determined based on the obtained x and y values. The database search system according to claim 3.