Vchunk join an efficient algorithm for edit similarity joins
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Vchunk join an efficient algorithm for edit similarity joins

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Similarity join is most important technique to ...

Similarity join is most important technique to
involve many applications such as data integration, record
linkage and pattern recognition. Here we introduce new
algorithm for similarity join with edit distance constraints.
Currently extracting overlapping grams from string and consider
only string that share certain gram as candidate. Now we propose
extracting non-overlapping substring or chunk from string.
Chunk scheme based on tail-restricted chunk boundary
dictionary (CBD). This approach integrated existing approach
for calculating similarity with several new filters unique to chunk
based method. Greedy algorithm automatically select good
chunking scheme from given data set. Then show the result our
method occupies less space and faster performance to compute
the value

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  • 1. VCHUNK JOIN An efficient algorithm for Edit similarity join Vignesh Babu, M.R.J. Computer Science and Engineering K.L.N. College of Information Technology, Sivagangai, India vigneshbabumrj@gmail.com Thanush Kodi, N. and Vijay Koushik, S. Computer Science and Engineering K.L.N. College of Information Technology, Sivagangai, India nthanushkodi@gmail.com svijaykoushik@gmail.com Abstract— Similarity join is most important technique to involve many applications such as data integration, record linkage and pattern recognition. Here we introduce new algorithm for similarity join with edit distance constraints. Currently extracting overlapping grams from string and consider only string that share certain gram as candidate. Now we propose extracting non-overlapping substring or chunk from string. Chunk scheme based on tail-restricted chunk boundary dictionary (CBD). This approach integrated existing approach for calculating similarity with several new filters unique to chunk based method. Greedy algorithm automatically select good chunking scheme from given data set. Then show the result our method occupies less space and faster performance to compute the value Keywords—chunk bound dictionary; Edit similarity joins, approximate string matching, prefix filtering, q-gram, edit distance I. INTRODUCTION This project will design of an efficient algorithm to reduce the space requirements for edit similarity join using the rank method to reduce the overlapping of filtered data form the dataset during data integration process. A similarity join finds pairs of objects from two datasets such that the pair‟s similarity value is no less than a given threshold. Early research in this area focuses on similarities defined in the Euclidean space; current research has spread to various similarity (or distance) functions. Similarity joins with edit distance constraint of no more than a constant. The major technical difficulty lies in the fact that the edit distance metric is complex and costly to compute. In order to scale to large datasets, all existing algorithms adopt the filter-and-verify paradigm. This first eliminates non-promising string pairs with relatively inexpensive computation and then performs verification by the edit distance computation. Gram-based methods extract all substrings of certain length (q-grams), or according to a dictionary (VGRAMs), for each string. Therefore, subsequent grams are overlapping and are highly redundant. This results in large index size and incurs high query processing costs when the index cannot be entirely accommodated in the main memory. The total size of the q- gram six time more than text string. Our observation is that the main redundancy in q-gram based approach in existing overlapping gram. In each string, we divide it into several disjoint substrings, or chunks, and we only need to process and index these chunks to improve the space usage. Chunk based robust chunking scheme that have salient property for each string. Robust chunking scheme based on tail-restrict Chunk Boundary Dictionary (CBD). Edit distance not more than the constant threshold. Here applying prefix filtering and location-based mismatch filtering methods, the number of signatures generated by our algorithm is guaranteed to be fewer than or equal to that of the q-grams- based method, q>= 2. Another feature of chunk based method that use existing filter and several new filtering unique to chunk based method including rank, chunk number and virtual CBD filtering. We also consider the problem of selecting a good chunking scheme for a given dataset. II. PROBLEM DEFINITION A. Existing System Previously similarity join with edit distance was used to extract overlapping grams from strings. Q-Gram is continuous substring of the length Q of the given string; we then find the position of the string to be matching. If they have same content and positions are within the edit distance, it performs an operation to calculate similarity through length, count position, and prefix filtering of the data from the dataset. The Q-Gram method index value are very large and then fixed length chunk based method‟s index values are huge, and variable gram(VGRAM) dictionary based method chunk does not predict easily and this complicates the work to implement this approach. B. Proposed System In our proposed system edit similarity join based on extracting non-overlapping substring, or chunk from string. A class of chunk based method notion tail-restricted chunk boundary dictionary uses several existing filtering and new powerful filtering techniques to reduce the size of index. Our proposed algorithm performs length, position, prefix filtering, content-based filtering and location-based filtering approach help to reduce the amount of space required to calculate the similarity of only the satisfied similar data. Resulting algorithm we call it as VChunkJoin-NoLC. Here we introduce rank,
  • 2. chunk number and virtual CBD algorithm to improve the efficiency of the problem. This approach should satisfy the edit distance value which is less than the threshold value will be calculated as the similarity value. III. INITIAL OPERATIONS To perform similarity join operation on data strings, first datasets must be gathered and should be parsed. Then parsed data is added to the database. A. Dataset Gathering The first step is to gather datasets for the similarity calculation. It was decided to use XML datasets for this purpose. XML data sets are generic in nature and can be used by software components of different platforms. A data dataset (or data set) is a collection of data. Most commonly a dataset corresponds to the contents of a single database table, or a single statistical data matrix, where every column of the table represents a particular variable, and each row corresponds to a given member of the dataset in question. The dataset lists values for each of the variables, such as height and weight of an object, for each member of the dataset. Each value is known as a datum. The dataset may comprise data for one or more members, corresponding to the number of rows. The term dataset may also be used more loosely, to refer to the data in a collection of closely related tables, corresponding to a particular experiment or event. Here the term dataset refers to collection of data in an XML file. It was decided to use IMDB, DBLB, TREC, UNIREF, UNRON datasets for this algorithm which usually have large number of similar values. B. Xml Parsing XML parsing or Pull parsing treats the document as a series of items which are read in sequence using the Iterator design pattern. This allows for writing of recursive-descent parsers in which the structure of the code performing the parsing mirrors the structure of the XML being parsed, and intermediate parsed results can be used and accessed as local variables within the methods performing the parsing, or passed down (as method parameters) into lower-level methods, or returned (as method return values) to higher- level methods. Here we use XML parsing or pull parsing to extract the data from the datasets chosen. These extracted data are then inserted into the database using SQL queries. At least two datasets are needed for similarity calculation. IV. DATA FILTERING The next step is to filter out the data that are likely to be similar. Two data are considered similar by different methods like the most used Prefix filtering. But on analysis prefix filtering method produces overlapping data as filtered result. This overlapped data occupy a large space in the memory. So to overcome this problem it was decided to use rank based filtering method to reduce the overlapping of data. A. Prefix Filtering Prefix Filtering method filters documents that have fields containing terms with a specified prefix. It is similar to phrase query, except that it acts as a filter. It can be placed within queries that accept a filter. This approach is applied to two data sets that we have taken before. In this approach the first dataset is considered ad prefix and the second dataset is considered as the suffix. For the filtering the length of the prefix string is compared with that of the suffix string. If the prefix length matches the suffix length, then they are filtered out and placed in a separately. Consider two datasets P and Q on which prefix filtering is to be applied. The filtered elements are placed in the set F such that F= {x | length(P(m)) is equal to length (Q(n))} Where, x – The filtered element. lenth(P(m)) – The length of the string m of the set P. length(Q(n)) – The length of the string n of the set Q. Prefix filtering method was fast but it yielded a large number of overlapping data which occupied too much space in memory. B. Rank Based Filtering The existing prefix filtering method occupied a large amount of memory, so we needed to use a new approach to reduce space occupancy by the filtered data. Thus we introduced a rank based method to reduce the overlapping data. In Rank based approach we consider only the overlapped data. Other filtered elements are kept as such. Here we apply rank to the overlapped data on both datasets. The rank of both elements whose difference is greater than the chosen threshold is taken for further processing and rest are ignored. Rank calculation involves certain Combination operations to obtain rank of the string. F={x│x is equal to rank}
  • 3. Where, rank = │rank(p(m)) – rank(Q(n))│≥ τ τ- Threshold value. rank(P(m)) – Rank of the string m of the dataset P. rank(Q(n)) – Rank of the string n of the dataset Q. By this approach most of the overlapping data are ignored for the next process. V. SIMILARITY CALCULATION The final step is to calculate the similarity between the strings of the filtered strings. The Similarity calculation involves the following steps. 1. Edit distance calculation. 2. Chunk the strings. 3. Similarity calculation. A. Edit Distance Calculation In this module we calculate the edit distance value from the matched text string from two dataset values. Edit distance calculation have to be performed by dynamic programming concept to get the minimum number of operation required to perform insert, delete, substitute and transform the string. Consider two string where both the strings have seen the length and position of the string are same the edit distance value as zero otherwise the value taken as how many operation need to transform one string to another as the edit distance value. ( ) { ( ) ( ) ( ) ( ) Where, D (i,j) – The value of the leading diagonal element in matrix. si – ith element of the string s. tj – jth element of the string t. The various operations are.  Insertion.  Deletion.  Substitution.  Transformation. B. String Chunking Chunks are very basic and necessary condition to perform similarity value between the two selected similar data from dataset. Chunks are the disjoint substring of the given string. Our proposed dataset TREC as book oriented dataset in this paper CBD selection have to be done by the character set {a,e,g,l,s,v}. We split the string or substring into the given set of data as chunk and taken the number of chunk in the string as input condition to perform similarity value calculation. It should be less than the constant threshold value. Example: Consider the string „Adolescence‟. According to the chunking rule above the string would be chunked as follows (underlined). A dol e s cence Thus the string Adolescence has been chunked into 5 parts. C. Similarity Calculation Similarity calculation is the final result of this algorithm. We have to perform the similarity value calculation between the two datasets. It depends on the edit distance value. This similarity value calculation is performed by which data are less than threshold and between the two set ranks should less than the threshold value. Similarity value is calculated by subtracting Edit distance value from the bigger length then divided by the same value as similarity value between the data [ ] VI. EXPERIMENTS We use the following five categories of algorithms in the experiment, all of which are implemented as in-memory algorithms, with all their inputs loaded into memory before running:  Fixed length gram based: Ed-Join [8] and Winnowing  [16].  VGRAM based: VGram-Join [11], [12].  Tree based: Bed -tree [21] and Trie-Join [22].  Enumeration based: PartEnum [7] and NGPP [23].  Vchunk based: VChunkJoin and VChunkJoin-NoLC. VChunkJoin is our proposed algorithm equipped with all the filterings and optimizations. When comparing with the VGRAM-based method, we remove location-based and content-based filterings, and name the resulting algorithm VChunkJoin-NoLC.
  • 4. VII. CONCLUSION Here we have introduced Novel approach to process edit similarity join efficiently based on partition strings into non- overlapping chunks. We have designed an efficient edit similarity join algorithm to incorporate all existing filter which occupies less space than the existing filtering methods and it‟s more powerful than the existing algorithm to calculate the similarity value between the two set of strings. The existing q- gram and VGRAM approaches occupies more amount of index value and unnecessary condition strings. To perform similarity value calculation it has to overcome through our proposed algorithm VChunkJoin. CBD selection algorithm is helpful to reduce the index size of the string and dynamic program is used to perform the edit distance value of the two dataset. REFERENCES [1] Arasu, A. Ganti, V. and Kaushik, R. “Efficient exact set-similarity joins,” in VLDB, 2006. [2] Behm, A. Ji, S. Li, S. and Lu, J. “Space-constrained gram-based indexing for efficient approximate string search,” in ICDE, 2009. [3] Elmagarmid, A. Ipeirotis, K. P. G. and Verykios, V. S. “Duplicate record detection: A survey,” TKDE, vol. 19, no. 1, pp. 1–16, 2007 [4] Li, C. Wang, B. and Yang, X. “VGRAM: Improving performance of approximate queries on string collections using variable-length grams,” in VLDB, 2007. [5] Manber, U. “Finding similar files in a large file system,” in USENIX Winter, 1994, pp. 1–10. [6] Ramasamy, Patel, J. M. Naughton, J. F. and Kaushik, R. “Set containment joins: The good, the bad and the ugly,” in VLDB, 2000, pp. 351–362. [7] Sarawagi, S. and Kirpal, A. “Efficient set joins on similarity predicates,” in SIGMOD, 2004. [8] Wang, J. Feng, J. and Li, G. “Trie-join: Efficient trie-based string similarity joins with edit,” in VLDB, 2010. [9] Xiao, C. Wang, W. Lin, X. and Yu, J. X. U. “Efficient similarity joins for near duplicate detection,” in WWW, 2008. [10] Xiao, C. Wang, W. and Lin, X. “Ed-Join: an efficient algorithm for similarity joins with edit distance constraints,” PVLDB, vol. 1, no. 1, pp. 933–944, 2008. [11] Yang, X. Wang, B. and Li, C. “Cost-based variable-length-gram selection for string collections to support approximate queries efficiently,” in SIGMOD Conference, 2008, pp. 353–364.