Dictionary Compression Reducing Symbolic Redundancy in RDF Antonio - PowerPoint PPT Presentation

Image: ALCÁZAR (S EGOVIA , SPAIN ) Dictionary Compression Reducing Symbolic Redundancy in RDF Antonio Fariña, Javier D. Fernández and Miguel A. Martinez-Prieto 3rd KEYSTONE Training School Keyword search in Big Linked Data 23 TH AUGUST 2017

Agenda Introduction  What is Dictionary Compression?  Compressed String Dictionaries  Some Experimental Numbers  RDF Dictionaries  Foundations  RDF Dictionary-based Compression  Dictionaries in Practice  Conclusions  PAGE 2 images: zurb.com

Dictionary Compression Introduction • What is Dictionary Compression? • Compressed String Dictionaries

What is Dictionary Compression? “ Dictionary compression is a simple but effective technique which replaces the occurrences of terms by identifiers which are more compact to encode and easier and more efficient to handle. PAGE 4 DICTIONARY COMPRESSION

Dictionary Compression Dictionary Compression Dictionary compression is a simple but effective technique which replaces the occurrences of (long, variable-length) terms by (short) identifiers which are more compact to encode and easier and more efficient to handle. Implementing this class of compression requires an efficient data  structure configuration (dictionary) which provides, at least, two basic mapping operations: locate(t) returns i if the term t is the i-th element in the dictionary.  extract(i) returns the i-th term ( t ) in the dictionary.  The dictionary organizes all different terms (vocabulary) in the dataset.  Dictionary compression has been traditionally applied for natural language  processing purposes (e.g. information retrieval). PAGE 5 DICTIONARY COMPRESSION

Dictionary Compression ID String 1 he … 2 la 3 niña la tarara sí 4 la tarara no no la tarara niña 5 que que la he visto yo 6 sí … 7 tarara 8 visto 9 yo data structure PAGE 6 DICTIONARY COMPRESSION

Dictionary Compression ID String 1 he … 2 la 3 niña 2 7 6 4 2 7 4 no 2 7 3 5 que 5 2 1 8 9 6 sí … 7 tarara 8 visto 9 yo data structure PAGE 7 DICTIONARY COMPRESSION

Dictionary Compression The original text takes 59 bytes 59 chars * 1 byte/char … la tarara sí la tarara no la tarara niña que la he visto yo … PAGE 8 DICTIONARY COMPRESSION

Dictionary Compression The original text takes 59 bytes 59 chars * 1 byte/char … The dictionary compressed text takes 7 bytes 2 7 6 14 IDs * log 2 (9) bits/ID 2 7 4 + 2 7 3 the cost of serializing 5 2 1 8 9 the data structure. … PAGE 9 DICTIONARY COMPRESSION

Dictionary Compression Dictionary Compression is used for optimizing applications of…  Natural Language Processing (e.g. Information Retrieval or  Machine Translation) Web Graph Management .  Triplestores (e.g. RDF3X) and other semantic tools (e.g.  HDT) NoSQL databases .  Bioinformatics search engines .  Internet Routing .  Geographic Information Systems .  ….  PAGE 10 DICTIONARY COMPRESSION

Data Structures Dictionaries have been traditionally implemented using  well-known data structures: Hash tables or tries for resolving locate queries.  Arrays for resolving extract queries.  These solutions are efficient, but require high amounts  of memory for using them in practical scenarios. PAGE 11 DICTIONARY COMPRESSION

The Problem … Data sets are increasingly bigger and more varied:  Vocabularies are also larger and comprise more heterogeneous terms.  The dictionary size is a bottleneck for applications running under  restrictions of main memory. The resulting dictionary data structure is very large and do not scale for  efficient in-memory management: Dictionary management is becoming a scalability issue by itself and it  must be optimized for Big Data scenarios. Preconditions:  Dictionaries are static (they are rebuilt from the scratch when the vocabulary is  changed). Dictionaries are cached in main memory.  PAGE 12 DICTIONARY COMPRESSION

Compressed String Dictionaries “ Compressed String Dictionaries are a particular class of compacta data structure which is optimize for dealing with string vocabularies from different domains. PAGE 13 DICTIONARY COMPRESSION

The Solutions … Innovative compressed string dictionaries are proposed for managing big  vocabularies in main memory: Traditional dictionaries are revisited for optimizing their memory footprint.  Existing compact data structures are tuned to perform as dictionaries.  New compact data structures has been designed as compressed string  dictionaries. All these techniques ensure efficient in-memory query resolution :  locate and extract are resolved at microsecond level.  New interesting queries are also supported by these techniques:  Prefix-based queries retrieve IDs / terms matching a given prefix.  Substring-based queries retrieve IDs / terms matching a given substring.  PAGE 14 DICTIONARY COMPRESSION

Queries ID String locate (“ tarara ”) = 7 1 he extract (2) = “la” 2 la 3 niña locatePrefix (“n”) = {3,4} 4 no extractPrefix (“n”) = {“ niña ”,”no”} 5 que 6 sí locateSubstring (“a”) = {2,3,7} 7 tarara extractSubstring (“a”) = {“la”,” niña ”,” tarara ”} 8 visto 9 yo PAGE 15 DICTIONARY COMPRESSION

Techniques for Compressing Dictionaries Compressed Hash :  The hash table is simulated using bitmaps.  Strings are stored in compressed form ( Huffman/Re-Pair ).  locate / extract operations are implemented using rank / select .  Differential Front-Coding Compression :  Front-Coding exploits that consecutive strings (in the vocabulary) are likely to share  a common prefix. Plain Front-Coding dictionaries use byte-oriented compression.  Compressed Front-Coding dictionaries combines HuTucker and Huffman/Re-Pair  compression. Primitive and prefix-based operations are implemented using binary search and  efficient sequential decoding . Self-Indexes :  The FM-Index is adapted to perform as dictionary and the XBW introduce a self-  indexed trie. All operations are implemented exploiting the BWT features .  PAGE 16 DICTIONARY COMPRESSION

More Details … PAGE 17 DICTIONARY COMPRESSION

Some Experimental Numbers “ Compressed String Dictionaries answer queries at the level of microseconds, while compressing vocabularies up to 20 times. PAGE 18 DICTIONARY COMPRESSION

Experimental Setup We analyze compression effectiveness and retrieval speed:  locate, extract .  Prefix-based operations (URIs)  Substring-based operations (Literals).  In practice, extract is the most important query :  It is used many times as results are retrieved from the compressed dataset.  26,948,638 URIs from Uniprot :  Averaged length: 51.04 chars per URI.  Highly-repetitive.  27,592,013 Literals from DBpedia :  Averaged length: 60.45 chars per Literal.  PAGE 19 DICTIONARY COMPRESSION

Locate / Extract Performance (URIs) PFC is the faster choice for locate/extract … locate ≈ 1.6 μ s/string.  extract ≈ 0.3-0.6 μ s/ID.  ..but requires more space:  ≈ 9 − 19 % of the original space .  HTFC (compressed Front-Coding) reports the most balanced space/time tradeoffs: locate ≈ 2.2-3 μs /string .  extract ≈ 0.7-1.6 μs /ID.  ≈ 5 − 13 % of the original space .  PAGE 20 DICTIONARY COMPRESSION

Locate / Extract Performance (Literals) HTFC reports the best compression ratios, but its performance is less competitive: locate ≈ 2-2.5 μs /string .  extract > 2.5 μs /ID.  ≈ 12 % of the original space.  HashDAC-rp (compressed Hashing) reports the best tradeoffs: locate ≈ 1.5 μs /string .  extract ≈ 1μs/ID .  ≈ 15 % of the original space .  PAGE 21 DICTIONARY COMPRESSION

Domain Entity Retrieval (URIs) PFC is the best choice for prefix-based operations: Although it uses more space than the other approaches.  PAGE 22 DICTIONARY COMPRESSION

Full-Text Search (Literals) Self-index based dictionaries are the only ones providing fullt-text search: FMI is the fastest solution (≈  1μs/result) when uses more space than the original vocabulary. XBW is the better choice for  this scenario: ≈ 5-6 μs /result.  ≈ 40% of the original space .  PAGE 23 DICTIONARY COMPRESSION

Dictionary Compression RDF Dictionaries • Foundations • RDF Dictionary-based Compression • Dictionaries in Practice

Foundations “ RDF Dictionaries are a core component of any compression or indexing approach desgined for semantic datasets. PAGE 25 DICTIONARY COMPRESSION

Basics An RDF dictionary comprises all different terms used in the dataset:  Terms are drawn from 3 disjoint vocabularies: URIs , Literals , and blank nodes .  URIs are medium-size strings which share long prefixes:  http://example.org/property/age http://example.org/property/location http://example.org/person/abe-simpson http://example.org/person/bart-simpson Literals tends to be large-size strings (with no predictable features), or  numbers, or dates…: “742 Evergreen Terrace” “Bart Simpson” “Homer Simpson” 10 Blank node serialization is not standardized:  “Auto - incremental” strings are usually used → similar features than URIs.  PAGE 26 DICTIONARY COMPRESSION