CN101271478A - Read-only interest point data base compression and storage method based on clustering block - Google Patents
Read-only interest point data base compression and storage method based on clustering block Download PDFInfo
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- CN101271478A CN101271478A CNA2008101060362A CN200810106036A CN101271478A CN 101271478 A CN101271478 A CN 101271478A CN A2008101060362 A CNA2008101060362 A CN A2008101060362A CN 200810106036 A CN200810106036 A CN 200810106036A CN 101271478 A CN101271478 A CN 101271478A
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Abstract
The invention discloses a read-only interest point database compression storage method based on clustering blocks, which includes the steps: (1) the records of interest points are clustered according to provinces, cities, districts and categories; (2) the records in the same category are arranged according to a longitude coordinate, a latitude coordinate and a telephone in an ascending order; (3) a plurality of pieces of records are combined into one recording block through a certain strategy; (4) different attributes in the records in the block are compressed through different compression technologies; (5) the ID of the last piece of records in the block is used as the block ID and the recording block is stored as a whole. In view of the characteristics of the read-only interest point database, the read-only interest point database compression storage method based on clustering blocks of the invention introduces a plurality of compression technologies, thus improving the space utilization and having smaller extra overhead, more particularly being applied to the retrieval of POI.
Description
Technical field
The present invention relates to a kind of read-only interest point data base compression and storage method, belong to the navigational system category of intelligent transportation field based on clustering block towards navigation terminal.
Background technology
In map of navigation electronic, comprising a large amount of point of interest (POI, Point Of Interest) information, for example communal facility, scenic spot, public place of entertainment or the like, the driver easily finds the destination with help, and gives prompting directly perceived, accurate in traveling process.The user has considerable part to depend on the degree of enriching of POI information in the map of navigation electronic to the satisfaction of navigation product.Vehicle mounted guidance development the earliest, the state-of-the-art Japan of technology. POI is 1,200 ten thousand in the map of navigation electronic, and POI reaches 5,000,000 in China's electronic chart at present, and prediction will reach 2,000 ten thousand in the future at least.
POI information generally comprises following attribute: classification, affiliated province, city and region, title, address, phone, longitude, latitude, and the above two generally are used for creating index, and other attribute all needs be saved in the database as data.
Early stage database compression great majority are to classify the compression granularity as with attribute, adopt some classical lossless compression algorithms such as general-purpose algorithms such as dictionary compression, Huffman encoding to compress.The shortcoming of this compression method is do not consider data in the database structural, and database is compressed with the form of data stream, and compressibility is not high.Up in recent years, the research of database compression method is just turned to the research of the special-purpose compression method of database, the achievement that has occurred at present has:
(1) block-oriented increment compression method (Wee K.NG, Chinya V.Ravishankar.Relational databasecompression using augmented vector quantization[C] .In Proc.Of ICDE, pages 540-549,1995).This method is with the database data piecemeal, and sequence number tuple placed in the middle is the representative tuple in each piece, and other tuples can be represented with the mode that the representative tuple adds increment in the piece.Its compression method is that the method for the tuple in the piece with vector quantization quantized, uses during compression by the coding of vector quantization generation and the common ordered pair of forming of increment of each tuple and its representative tuple and compresses tuple, and then the compression of fulfillment database.The advantage of block-oriented increment compression method is effective compressed database and can carry out the query manipulation of compressed database, therefore shortcoming is also to need to quantize for character string, only is applicable to that tuple comprises the situation of enumeration type character string and numerical value among a small circle.
(2) order-preserving compress technique (Gennady Antoshenkov, David B.Lomet, James Murray.Order preservingstring compression[J] .In Proc.OfICDE, pages 655-663,1996).This method is to adopt the compression dictionary coding that a plurality of string attribute values of database are compressed at first.It is split as a plurality of small characters strings with character string, utilize elongated dictionary encoding method to these small characters string compressions, the method of its fractionation depends on the coded sequence of encoder dictionary, and the SEQ.XFER table that has a same sequence with encoder dictionary has then guaranteed the realization of order-preserving.Because the order-preserving compression simply is easy to realize that many subsequently universal coding modes such as Huffman encoding, arithmetic coding all are applied in the order-preserving compress technique.Its advantage is to be easy to realize that shortcoming is that compressibility is not high, and only is applicable to the situation of a plurality of character strings.
(3) based on compress technique (the Shivnath Babu of semanteme, Minos N.Garofaiakis, Rajeev Rastogi.Spartan:Amodel-based semantic compression system for massive data tables ([J] .In SIGMOD, 2001)).This is that a kind of semanteme that utilizes attribute and data mining model are realized the method compressed.This method is utilized foreseeable data relationship and is come to make up classification decline tree-models for all row of whole tables of data for the fault-tolerant way of individual attribute appointment, select a particular subset of property set during compression, the value of not compressed in this subclass, the decline tree then adopts certain learning method and Combinatorial Optimization algorithm to come these values are estimated, and result, i.e. compressed value are estimated in generation.Realize the compression of entire database by the property set subclass of bringing in constant renewal in the decline tree.Its foreseeable data relationship is based on the relation of attribute semanteme, and data mining then is applied in the prediction computing of decline tree.Compress technique based on semanteme is the database compression method that diminishes, owing to utilized the semanteme of attribute, needs the at first semantic information of analytic attribute field before the decline tree compresses, thereby this compression method is complicated and be difficult for realizing, and compressibility is not high yet.
(4) magnanimity relation split compress technique (Luo Jizhou, Li Jianzhong, a kind of effective relational database compression method, the software journal, 16 (2), 2005:205-214).This method is a kind of special-purpose compression method that proposes at the magnanimity relational database that constantly occurs at present.This method is isolated little codomain set of properties from the magnanimity relational database, the magnanimity relation is split, and the new relation to little codomain set of properties place compresses then.Need to estimate to split earlier the ratio of compression of compression during compression,, otherwise abandon splitting compression if ratio of compression rationally then split compression.Magnanimity relation fractionation compress technique has effect preferably to the compression and the second-compressed of high-volume database, still because the NP completeness of the identification problem of little codomain set of properties makes to split and compress complexity that cost is excessive, and only is applicable to the attribute of little codomain.
And, generally have following characteristics for POI database towards navigation:
The longitude and latitude of same province, city and region and phone be Discrete Distribution in a limited scope.
Though the title of different POI has uniqueness mostly under the same classification, it is higher that some everyday words repeats ratio, such as generally all containing this speech of company in the POI title of company's classification.
The ratio that contains repetition prefix character string under the same province, city and region in the address of different POI is higher.
Because POI information has the stability of long period, so generally be exclusively used in retrieval, the situation that does not have deletion and revise.
And POI generally needs by province, city and region and classification retrieval, and retrieval by name, also needs participle to set up index for Chinese.Consider above factor, also do not have a kind of existing method to be suitable at present towards the compression of the POI database of navigation.
Summary of the invention
Technical matters to be solved by this invention: overcome the deficiencies in the prior art, a kind of read-only interest point data base compression and storage method based on clustering block is provided, this method effectively raises space availability ratio, and make the scope that decompresses only limit in the piece, overhead is less, is particularly suitable for the retrieval of POI.
Technical solution of the present invention:, realize by following steps based on the read-only interest point data base compression and storage method of clustering block:
(1) point of interest POI record is carried out cluster according to province, city and region and classification;
(2) record of the POI in the same class being carried out ascending order according to longitude coordinate-latitude coordinate-phone arranges;
(3) many POI records are combined into a record block;
(4) in described record block, adopt different compression methods to compress to each territory in the POI record;
(5) with the ID of the last item POI record in the described record block as piece ID, the record block after the compression is stored as a whole.
Described step (3) with the method that many records are combined into a record block is:
With a disk block size is limit value, and point of interest records all in the same class is carried out piecemeal according to shared storage size, makes each piece capacity be not more than this limit value.
Described step (4) adopts different compression methods to comprise to territories different in the POI record in record block: attribute-latitude and longitude coordinates and telephone number for numeric type use the difference compression, promptly except that article one record, the longitude coordinate of trailer record, the storage of latitude coordinate and telephone number be difference with a last record, described difference length is unfixing, need to discern by flag, described telephone number may not have, and needs to discern by flag.
Difference compression specific implementation method is as follows:
(1) at first converts floating number to integer;
(2) article one writes down in piece, the longitude coordinate of trailer record in the piece, the difference of latitude coordinate and a telephone number storage and a last record, this difference length is fixing, scope according to difference size place may be 1-4 byte, need identify by two flag;
(3) telephone number may not have, and need identify by a bit.
Described step (4) adopts different compression methods to comprise to territories different in the POI record in record block: for the territory of character string type, promptly following method is adopted in title and address:
(1) adopt the dictionary code compression method that title is compressed;
(2) adopt the prefix compression method that the address is compressed.
The prerequisite of described dictionary encoding compression is name to be called the participle pre-service, the pretreated method of described participle is that the speech that at first title of all records in the piece is comprised is added up, when the frequency of occurrences of certain speech surpasses twice, be about to this speech and extract the dictionary district that is stored in the first equivalent length of piece.
Described dictionary code compression method is that the speech that will be extracted in the title comes coded representation with the speech of this speech sequence number long and in institute equivalent long word allusion quotation district, in order to separate with the block that is not extracted, each title all has a bitmap index to identify simultaneously.
Described prefix compression method is that the maximum-prefix character string in the address is come coded representation with the LSN and the string length that occurred before it, discerns by flag whether the prefix compression is arranged simultaneously.
The present invention's advantage compared with prior art is:
(1) the present invention is not limited to the data of particular type, can compress for the data of numeric type and character string type.For example, the order-preserving compress technique of mentioning in the background technology only is applicable to the situation of a plurality of character strings; Block-oriented increment compression method then needs character string is quantized, prerequisite be the kinds of characters string number that occurs seldom, repetition rate is very high; It also is the situation that only is applicable to little codomain that the magnanimity relation splits compress technique.And for the POI database, character string type is arranged not only, and also have value type simultaneously, and the title monopoly of character string type being very strong, the record probability of occurrence with same names is very low, and the method that adopts background technology to introduce is obviously improper.Therefore, our logarithm value type and character string type are handled respectively, adopt different compression methods, and will repeat the bigger speech of probability in the title, and the prefix character string that repeats in the address extracts, and encode to reach the purpose of compression.
(2) made full use of the characteristic of POI database.For example, in the block-oriented increment compression method of mentioning in the background technology, piecemeal just simply will be by the size that takes up room as foundation.And for the POI database, will write down earlier according to province, city and region and classification and carry out cluster, and then according to carrying out piecemeal in the space, can be so that redundance be higher in the piece, thus reach better compression effectiveness.Simultaneously, because POI generally needs to press province, city and region and classification retrieval, and retrieval by name, also need participle to set up index for Chinese.Therefore, carry out cluster according to province, city and region and classification and can also satisfy mode, and the dictionary for word segmentation of using will retrieve the time is applied in the compression simultaneously as compressing dictionary, also makes compression method become simple by the retrieval of province, city and region and classification.
(3) when effectively raising space availability ratio, also be suitable for the retrieval of POI.Because in the database of storing in B tree mode, the leaf node of tree is the size of a disk block normally, after the compression, just can store more record at same inter-node according to the method described above, improved operating factor of memory space, I/O number when also reducing retrieval simultaneously.For category retrieval, behind the piece that decompress(ion) satisfies condition, records all in the piece as a result of collected to return get final product; For retrieval by name, the piece at the record place that satisfies condition is transferred to the internal memory from disk, the scope of decompression also only limits in the piece, and the result behind the decompress(ion) can leave among the cache simultaneously, with convenient follow-up retrieval.
Description of drawings
Fig. 1 is overall flow figure of the present invention;
Fig. 2 is the piecemeal process flow diagram;
Fig. 3 is a piece stored structural drawing;
Fig. 4 is compression process figure in the piece.
Embodiment
The said read-only interest point data base compression and storage method flow process based on clustering block of the present invention as shown in Figure 1,
1, the point of interest record is carried out cluster according to province, city and region and classification;
2, the record in the same class is carried out ascending order according to longitude coordinate-latitude coordinate-phone and arrange, and give unique ID with the POI record with this order;
3, record is carried out pre-service:
(1) because six precision is 0.1 meter behind the radix point of coordinate, is enough to satisfy the requirement of general communication navigation, so longitude coordinate and latitude coordinate can be multiply by 10
6, represent with the signless integer of four bytes.
(2) title is carried out word segmentation processing.Not only can be used for compression, can also set up glossarial index, realize title retrieval POI.Because title is generally all shorter, therefore can get speech long is 2-4 word.
4, in same class, many records are combined into a record block.As shown in Figure 2, order travels through all records in the same class, whether judges institute's big or small sum that takes up space greater than limit value, if less than would continue to read in next bar record; Otherwise a record block formed in the record that will travel through, then with article one record continuation traversal piecemeal of next bar record as new piece.
5, as shown in Figure 3, before record block was compressed storage, at first the buffer zone of a disk block size of predistribution was divided into build district and data field.The build district comprises vocabulary district and record Head Section, and actual record is promptly stored in the data field.Wherein, each record-header takies 5 bytes, and structure is as shown in table 1, because record random length, so need to specify the length after each attribute compression and be recorded in offset address in the piece, the piece maximum limit is decided to be 64KB, so side-play amount can be with two byte representations.Be limited to 256 words (2 bytes) after title and the address compression on the length.
Table 1 recording head structure
Wherein, telephone number existence/address compression is designated: 0-does not exist/not compression; 1-existence/compression.
Length coding is as shown in table 2:
Table 2 length coding
| Coding | Meaning |
| 00 | 1 byte |
| 01 | 2 bytes |
| 10 | 3 bytes |
| 11 | 4 bytes |
The structure of record is as shown in table 3:
Table 3 interrecord structure
| Title compressing mark bitmap | Title | The address | Longitude | Latitude | Phone |
6, in piece, adopt different compression methods to compress to attributes different in the record.As shown in Figure 4, the flow process of compression is as follows in the piece:
(1) frequency of occurrences of speech in all titles in the statistics block is extracted the long vocabulary district of equivalent with occurrence number greater than 2 speech, and this speech is encoded, and is as shown in table 4:
Table 4 title Chinese word coding
Speech long codes: 00-2 words; The 01-3 words; The 10-4 words
(2) title compressing mark bitmap created in record in the traversal piece, and its effect is which word is a compressed encoding in the title of discerning after compressing, and which is common Unicode coding.Its length is by the decision of the title length in the record-header, and for example: title length is 5＜=8, and then bitmap lengths is 1 byte; Title length is 13＜=16, and then bitmap lengths is 2 bytes, by that analogy, each corresponding word (2 bytes), not compression of 0 representative, 1 representative has compression; Find out the LSN that the maximum address prefix in preceding 256 records, occurs simultaneously, address prefix is encoded, as shown in table 5; And coordinate and phone are carried out the difference compressed encoding, with the compression after recording storage in buffer zone.
(3) with the ID of the last item record in the piece as piece ID, build in the buffer zone and data field are stored as a whole.
Table 5 address prefix coding
| First byte | Second byte |
| The sequence number (preceding 256 skew) of maximum-prefix place record | Maximum-prefix length 25 |
Claims (10)
1,, it is characterized in that realizing by following steps based on the read-only interest point data base compression and storage method of clustering block:
(1) point of interest POI record is carried out cluster according to province, city and region and classification;
(2) record of the POI in the same class being carried out ascending order according to longitude coordinate-latitude coordinate-phone arranges;
(3) many POI records are combined into a record block;
(4) in described record block, adopt different compression methods to compress to each territory in the POI record;
(5) with the ID of the last item POI record in the described record block as piece ID, the record block after the compression is stored as a whole.
2, the read-only interest point data base compression and storage method based on clustering block according to claim 1 is characterized in that: described step (3) with the method that many records are combined into a record block is:
With a disk block size is limit value, and point of interest records all in the same class is carried out piecemeal according to shared storage size, makes each piece capacity be not more than this limit value.
3, the read-only interest point data base compression and storage method based on clustering block according to claim 1, it is characterized in that: described step (4) adopts different compression methods to comprise to territories different in the POI record in record block: attribute-latitude and longitude coordinates and telephone number for numeric type use the difference compression, promptly except that article one record, the longitude coordinate of trailer record, the storage of latitude coordinate and telephone number be difference with a last record.
4, the read-only interest point data base compression and storage method based on clustering block according to claim 3 is characterized in that: described difference length is unfixing, needs to discern by flag.
5, the read-only interest point data base compression and storage method based on clustering block according to claim 3, it is characterized in that: described telephone number may not have, and needs to discern by flag.
6, the read-only interest point data base compression and storage method based on clustering block according to claim 1, it is characterized in that: described step (4) adopts different compression methods to comprise to territories different in the POI record in record block: for the territory of character string type, promptly following method is adopted in title and address:
(1) adopt the dictionary code compression method that title is compressed;
(2) adopt the prefix compression method that the address is compressed.
7, the read-only interest point data base compression and storage method based on clustering block according to claim 6 is characterized in that: the prerequisite of described dictionary encoding compression is name to be called the participle pre-service.
8, the read-only interest point data base compression and storage method based on clustering block according to claim 7, it is characterized in that: the pretreated method of described participle is that the speech that at first title of all records in the piece is comprised is added up, when the frequency of occurrences of certain speech surpasses twice, be about to this speech and extract the dictionary district that is stored in the first equivalent length of piece.
9, the read-only interest point data base compression and storage method based on clustering block according to claim 6, it is characterized in that: described dictionary code compression method is that the speech that will be extracted in the title comes coded representation with the speech of this speech sequence number long and in institute equivalent long word allusion quotation district, in order to separate with the block that is not extracted, each title all has a bitmap index to identify simultaneously.
10, the read-only interest point data base compression and storage method based on clustering block according to claim 6, it is characterized in that: described prefix compression method is that the maximum-prefix character string in the address is come coded representation with the LSN and the string length that occurred before it, discerns by flag whether the prefix compression is arranged simultaneously.
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