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2021
Conference object
Open Access

**Fast and compact set intersection through recursive universe partitioning**

*Pibiri G. E.*

We present a data structure that encodes a sorted integer sequence in small space allowing, at the same time, fast intersection operations. The data layout is carefully designed to exploit word-level parallelism and SIMD instructions, hence providing good practical performance. The core algorithmic idea is that of recursive partitioning the universe of representation: a markedly different paradigm than the widespread strategy of partitioning the sequence based on its length. Extensive experimentation and comparison against several competitive techniques shows that the proposed solution embodies an improved space/time trade-off for the set intersection problem.**Source: **IEEE Data Compression Conference, Online Conference, 23-26/03/2021

**See at: **
ISTI Repository | CNR ExploRA

2021
Article
Open Access

**Rank/select queries over mutable bitmaps**

*Pibiri G. E., Kanda S.*

The problem of answering rank/select queries over a bitmap is of utmost importance for many succinct data structures. When the bitmap does not change, many solutions exist in the theoretical and practical side. In this work we consider the case where one is allowed to modify the bitmap via a flip(i) operation that toggles its i-th bit. By adapting and properly extending some results concerning prefix-sum data structures, we present a practical solution to the problem, tailored for modern CPU instruction sets. Compared to the state-of-the-art, our solution improves runtime with no space degradation. Moreover, it does not incur in a significant runtime penalty when compared to the fastest immutable indexes, while providing even lower space overhead.**Source: **Information systems (Oxf.) (2021).**Project(s): ** BigDataGrapes

**See at: **
ISTI Repository | CNR ExploRA

2021
Conference object
Open Access

**Compressed indexes for fast search of semantic data**

*Perego R., Pibiri G. E., Venturini R.*

The sheer increase in volume of RDF data demands efficient solutions for the triple indexing problem, that is devising a compressed data structure to compactly represent RDF triples by guaranteeing, at the same time, fast pattern matching operations. This problem lies at the heart of delivering good practical performance for the resolution of complex SPARQL queries on large RDF datasets. We propose a trie-based index layout to solve the problem and introduce two novel techniques to reduce its space of representation for improved effectiveness. The extensive experimental analysis reveals that our best space/time trade-off configuration substantially outperforms existing solutions at the state-of-the-art, by taking 30-60% less space and speeding up query execution by a factor of 2-81 times.**Source: **IEEE International Conference on Data Engineering (ICDE), 19-22/04/2021**Project(s): ** BigDataGrapes

**See at: **
ISTI Repository | CNR ExploRA

2020
Article
Open Access

**Compressed Indexes for Fast Search of Semantic Data**

*Pibiri G. E., Perego R., Venturini R.*

The sheer increase in volume of RDF data demands efficient solutions for the triple indexing problem, that is to devise a compressed data structure to compactly represent RDF triples by guaranteeing, at the same time, fast pattern matching operations. This problem lies at the heart of delivering good practical performance for the resolution of complex SPARQL queries on large RDF datasets. In this work, we propose a trie-based index layout to solve the problem and introduce two novel techniques to reduce its space of representation for improved effectiveness. The extensive experimental analysis, conducted over a wide range of publicly available real-world datasets, reveals that our best space/time trade-off configuration substantially outperforms existing solutions at the state-of-the-art, by taking 30 - 60% less space and speeding up query execution by a factor of 2-81× .**Source: **IEEE transactions on knowledge and data engineering (Print) (2020): 1–11. doi:10.1109/TKDE.2020.2966609**DOI: **10.1109/TKDE.2020.2966609**DOI: **10.1109/tkde.2020.2966609**Project(s): ** BigDataGrapes

**See at: **
arXiv.org e-Print Archive | IEEE Transactions on Knowledge and Data Engineering | ieeexplore.ieee.org | ISTI Repository | CNR ExploRA | IEEE Transactions on Knowledge and Data Engineering | IEEE Transactions on Knowledge and Data Engineering | IEEE Transactions on Knowledge and Data Engineering | IEEE Transactions on Knowledge and Data Engineering | IEEE Transactions on Knowledge and Data Engineering

2020
Conference object
Open Access

**Efficient and effective query auto-completion**

*Gog S., Pibiri G. E., Venturini R.*

Query Auto-Completion (QAC) is an ubiquitous feature of modern textual search systems, suggesting possible ways of completing the query being typed by the user. Efficiency is crucial to make the system have a real-time responsiveness when operating in the million-scale search space. Prior work has extensively advocated the use of a trie data structure for fast prefix-search operations in compact space. However, searching by prefix has little discovery power in that only completions that are prefixed by the query are returned. This may impact negatively the effectiveness of the QAC system, with a consequent monetary loss for real applications like Web Search Engines and eCommerce. In this work we describe the implementation that empowers a new QAC system at eBay, and discuss its efficiency/effectiveness in relation to other approaches at the state-of-the-art. The solution is based on the combination of an inverted index with succinct data structures, a much less explored direction in the literature. This system is replacing the previous implementation based on Apache SOLR that was not always able to meet the required service-level-agreement.**Source: **ACM Conference on Research and Development in Information Retrieval, pp. 2271–2280, 25/07/2020-30/07/2020**DOI: **10.1145/3397271.3401432**Project(s): ** BigDataGrapes

**See at: **
arXiv.org e-Print Archive | Unknown Repository | ISTI Repository | Unknown Repository | Unknown Repository | Unknown Repository | Unknown Repository | Unknown Repository | Unknown Repository | Unknown Repository | Unknown Repository | dl.acm.org | Unknown Repository | Unknown Repository | Unknown Repository | CNR ExploRA | Unknown Repository | Unknown Repository

2020
Article
Open Access

**Practical trade-offs for the prefix-sum problem**

*Pibiri G. E., Venturini R.*

Given an integer arrayA, theprefix-sum problemis to answersum(i)queries that return the sum of the elements inA[0..i], knowing that the integers inAcan be changed. It is a classic problem in data structure design with a wide range of applications in computing from coding to databases. In this work, we propose and compare practical solutions to this problem, showing that new trade-offs between the performance of queries and updates can be achieved on modern hardware.**Source: **Software, practice & experience (Print) (2020). doi:10.1002/spe.2918**DOI: **10.1002/spe.2918**Project(s): ** BigDataGrapes

**See at: **
arXiv.org e-Print Archive | Software Practice and Experience | Software Practice and Experience | Software Practice and Experience | Software Practice and Experience | onlinelibrary.wiley.com | Software Practice and Experience | Software Practice and Experience | Software Practice and Experience | CNR ExploRA

2020
Article
Open Access

**Techniques for Inverted Index Compression**

*Pibiri G. E., Venturini R.*

The data structure at the core of large-scale search engines is the inverted index, which is essentially a collection of sorted integer sequences called inverted lists. Because of the many documents indexed by such engines and stringent performance requirements imposed by the heavy load of queries, the inverted index stores billions of integers that must be searched efficiently. In this scenario, index compression is essential because it leads to a better exploitation of the computer memory hierarchy for faster query processing and, at the same time, allows reducing the number of storage machines. The aim of this article is twofold: first, surveying the encoding algorithms suitable for inverted index compression and, second, characterizing the performance of the inverted index through experimentation.**Source: **ACM computing surveys (2020).**Project(s): ** BigDataGrapes

**See at: **
arxiv.org | ISTI Repository | CNR ExploRA

2019
Conference object
Open Access

**Fast dictionary-based compression for inverted indexes**

*Pibiri G. E., Petri M., Moffat A.*

Dictionary-based compression schemes provide fast decoding operation, typically at the expense of reduced compression effectiveness compared to statistical or probability-based approaches. In this work, we apply dictionary-based techniques to the compression of inverted lists, showing that the high degree of regularity that these integer sequences exhibit is a good match for certain types of dictionary methods, and that an important new trade-off balance between compression effectiveness and compression efficiency can be achieved. Our observations are supported by experiments using the document-level inverted index data for two large text collections, and a wide range of other index compression implementations as reference points. Those experiments demonstrate that the gap between efficiency and effectiveness can be substantially narrowed.**Source: **International Conference on Web Search and Data Mining, pp. 6–14, 11/02/2019,15/02/2019**DOI: **10.1145/3289600.3290962

**See at: **
dl.acm.org | ISTI Repository | CNR ExploRA | Unknown Repository | Unknown Repository | Unknown Repository | Unknown Repository | Unknown Repository

2019
Article
Open Access

**Handling massive n-gram datasets efficiently**

*Pibiri G. E., Venturini R.*

Two fundamental problems concern the handling of large n-gram language models: indexing, that is, compressing the n-grams and associated satellite values without compromising their retrieval speed, and estimation, that is, computing the probability distribution of the n-grams extracted from a large textual source. Performing these two tasks efficiently is vital for several applications in the fields of Information Retrieval, Natural Language Processing, and Machine Learning, such as auto-completion in search engines and machine translation. Regarding the problem of indexing, we describe compressed, exact, and lossless data structures that simultaneously achieve high space reductions and no time degradation with respect to the state-of-the-art solutions and related software packages. In particular, we present a compressed trie data structure in which each word of an n-gram following a context of fixed length k, that is, its preceding k words, is encoded as an integer whose value is proportional to the number of words that follow such context. Since the number of words following a given context is typically very small in natural languages, we lower the space of representation to compression levels that were never achieved before, allowing the indexing of billions of strings. Despite the significant savings in space, our technique introduces a negligible penalty at query time. Specifically, the most space-efficient competitors in the literature, which are both quantized and lossy, do not take less than our trie data structure and are up to 5 times slower. Conversely, our trie is as fast as the fastest competitor but also retains an advantage of up to 65% in absolute space. Regarding the problem of estimation, we present a novel algorithm for estimating modified Kneser-Ney language models that have emerged as the de-facto choice for language modeling in both academia and industry thanks to their relatively low perplexity performance. Estimating such models from large textual sources poses the challenge of devising algorithms that make a parsimonious use of the disk. The state-of-the-art algorithm uses three sorting steps in external memory: we show an improved construction that requires only one sorting step by exploiting the properties of the extracted n-gram strings. With an extensive experimental analysis performed on billions of n-grams, we show an average improvement of 4.5 times on the total runtime of the previous approach.**Source: **ACM transactions on information systems 37 (2019). doi:10.1145/3302913**DOI: **10.1145/3302913**DOI: **10.5281/zenodo.3257995**DOI: **10.5281/zenodo.3257994**Project(s): ** BigDataGrapes

**See at: **
arXiv.org e-Print Archive | dl.acm.org | ISTI Repository | CNR ExploRA | Zenodo | ACM Transactions on Information Systems | ACM Transactions on Information Systems | ACM Transactions on Information Systems | ACM Transactions on Information Systems | ACM Transactions on Information Systems | ACM Transactions on Information Systems | ACM Transactions on Information Systems | ACM Transactions on Information Systems

2019
Article
Open Access

**On optimally partitioning variable-byte codes**

*Pibiri G. E., Venturini R.*

The ubiquitous Variable-Byte encoding is one of the fastest compressed representation for integer sequences. However, its compression ratio is usually not competitive with other more sophisticated encoders, especially when the integers to be compressed are small that is the typical case for inverted indexes. This paper shows that the compression ratio of Variable-Byte can be improved by 2× by adopting a partitioned representation of the inverted lists. This makes Variable-Byte surprisingly competitive in space with the best bit-aligned encoders, hence disproving the folklore belief that Variable-Byte is space-inefficient for inverted index compression. Despite the significant space savings, we show that our optimization almost comes for free, given that: we introduce an optimal partitioning algorithm that does not affect indexing time because of its linear-time complexity; we show that the query processing speed of Variable-Byte is preserved, with an extensive experimental analysis and comparison with several other state-of-the-art encoders.**Source: **IEEE transactions on knowledge and data engineering (Print) (2019). doi:10.1109/TKDE.2019.2911288**DOI: **10.1109/TKDE.2019.2911288**DOI: **10.1109/tkde.2019.2911288**Project(s): ** BigDataGrapes

**See at: **
arXiv.org e-Print Archive | IEEE Transactions on Knowledge and Data Engineering | ISTI Repository | IEEE Transactions on Knowledge and Data Engineering | IEEE Transactions on Knowledge and Data Engineering | IEEE Transactions on Knowledge and Data Engineering | IEEE Transactions on Knowledge and Data Engineering | ieeexplore.ieee.org | IEEE Transactions on Knowledge and Data Engineering | CNR ExploRA | IEEE Transactions on Knowledge and Data Engineering | IEEE Transactions on Knowledge and Data Engineering

2019
Doctoral thesis
Open Access

**Space and time-efficient data structures for massive datasets**

*Pibiri G. M.*

This thesis concerns the design of compressed data structures for the efficient storage of massive datasets of integer sequences and short strings.

**See at: **
etd.adm.unipi.it | CNR ExploRA

2018
Part of book or chapter of book
Open Access

**Inverted Index Compression**

*Pibiri G. E., Venturini R.*

The data structure at the core of nowadays large-scale search engines, social networks and storage architectures is the inverted index, which can be regarded as being a collection of sorted integer sequences called inverted lists. Because of the many documents indexed by search engines and stringent performance requirements dictated by the heavy load of user queries, the inverted lists often store several million (even billion) of integers and must be searched efficiently.
In this scenario, compressing the inverted lists of the index appears as a mandatory design phase since it can introduce a twofold advantage over a non-compressed representation: feed faster memory levels with more data in order to speed up the query processing algorithms and reduce the number of storage machines needed to host the whole index. The scope of the chapter is the one of surveying the most important encoding algorithms developed for efficient inverted index compression.**DOI: **10.1007/978-3-319-63962-8_52-1**DOI: **10.1007/978-3-319-77525-8_52

**See at: **
ISTI Repository | DOI Resolver | Unknown Repository | Unknown Repository | CNR ExploRA

2017
Article
Open Access

**Clustered Elias-Fano Indexes**

*Pibiri G. E., Venturini R.*

State-of-the-art encoders for inverted indexes compress each posting list individually. Encoding clusters of posting lists offers the possibility of reducing the redundancy of the lists while maintaining a noticeable query processing speed.**Source: **ACM transactions on information systems 36 (2017). doi:10.1145/3052773**DOI: **10.1145/3052773**Project(s): ** SoBigData

**See at: **
Archivio della Ricerca - Università di Pisa | ACM Transactions on Information Systems | ACM Transactions on Information Systems | ACM Transactions on Information Systems | ACM Transactions on Information Systems | ACM Transactions on Information Systems | dl.acm.org | ACM Transactions on Information Systems | CNR ExploRA

2017
Conference object
Open Access

**Dynamic Elias-Fano Representation**

*Pibiri G. E., Venturini R.*

We show that it is possible to store a dynamic ordered set S(n,u) of n integers drawn from a bounded universe of size u in space close to the information-theoretic lower bound and yet preserve the asymptotic time optimality of the operations. Our results leverage on the Elias-Fano representation of S(n,u) which takes EF(S(n,u))=n?log(u/n)?+2n bits of space and can be shown to be less than half a bit per element away from the information-theoretic minimum.
Considering a RAM model with memory words of ?(log u) bits, we focus on the case in which the integers of S are drawn from a polynomial universe of size u=n?, for any ?=?(1). We represent S(n,u) with EF(S(n,u))+o(n) bits of space and:
1. support static predecessor/successor queries in O(min{1+log(u/n),loglog n});
2. make S grow in an append-only fashion by spending O(1) per inserted element;
3. support random access in O(log n/loglog n) worst-case, insertions/deletions in O(log n/loglog n) amortized and predecessor/successor queries in O(min{1+log(u/n),loglog n}) worst-case time. These time bounds are optimal.**Source: **Annual Symposium on Combinatorial Pattern Matching, Varsavia, Polonia, 4-6/07/2017**DOI: **10.4230/LIPIcs.CPM.2017.30**DOI: **10.4230/lipics.cpm.2017.30

**See at: **
drops.dagstuhl.de | ISTI Repository | CNR ExploRA

2017
Conference object
Restricted

**Efficient Data Structures for Massive N-Gram Datasets**

*Pibiri G. E., Venturini R.*

The effcient indexing of large and sparse N-gram datasets is crucial in several applications in Information Retrieval, Natural Language Processing and Machine Learning. Because of the stringent efficiency requirements, dealing with billions of N-grams poses the challenge of introducing a compressed representation that preserves the query processing speed. In this paperwe study the problem of reducing the space required by the representation of such datasets, maintaining the capability of looking up for a given N-gram within micro seconds. For this purpose we describe compressed, exact and lossless data structures that achieve, at the same time, high space reductions and no time degradation with respect to state-of-The-Art software packages. In particular, we present a trie data structure in which each word following a context of fixed length k, i.e., its preceding k words, is encoded as an integer whose value is proportional to the number of words that follow such context. Since the number of words following a given context is typically very small in natural languages, we are able to lower the space of representation to compression levels that were never achieved before. Despite the significant savings in space, we show that our technique introduces a negligible penalty at query time.**Source: **International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 615–624, Tokyo, Giappone, 7-11/08/2017**DOI: **10.1145/3077136.3080798

**See at: **
Unknown Repository | Unknown Repository | Unknown Repository | Unknown Repository | dl.acm.org | Unknown Repository | CNR ExploRA