6,576 research outputs found
The Case for Learned Index Structures
Indexes are models: a B-Tree-Index can be seen as a model to map a key to the
position of a record within a sorted array, a Hash-Index as a model to map a
key to a position of a record within an unsorted array, and a BitMap-Index as a
model to indicate if a data record exists or not. In this exploratory research
paper, we start from this premise and posit that all existing index structures
can be replaced with other types of models, including deep-learning models,
which we term learned indexes. The key idea is that a model can learn the sort
order or structure of lookup keys and use this signal to effectively predict
the position or existence of records. We theoretically analyze under which
conditions learned indexes outperform traditional index structures and describe
the main challenges in designing learned index structures. Our initial results
show, that by using neural nets we are able to outperform cache-optimized
B-Trees by up to 70% in speed while saving an order-of-magnitude in memory over
several real-world data sets. More importantly though, we believe that the idea
of replacing core components of a data management system through learned models
has far reaching implications for future systems designs and that this work
just provides a glimpse of what might be possible
The Parallelism Motifs of Genomic Data Analysis
Genomic data sets are growing dramatically as the cost of sequencing
continues to decline and small sequencing devices become available. Enormous
community databases store and share this data with the research community, but
some of these genomic data analysis problems require large scale computational
platforms to meet both the memory and computational requirements. These
applications differ from scientific simulations that dominate the workload on
high end parallel systems today and place different requirements on programming
support, software libraries, and parallel architectural design. For example,
they involve irregular communication patterns such as asynchronous updates to
shared data structures. We consider several problems in high performance
genomics analysis, including alignment, profiling, clustering, and assembly for
both single genomes and metagenomes. We identify some of the common
computational patterns or motifs that help inform parallelization strategies
and compare our motifs to some of the established lists, arguing that at least
two key patterns, sorting and hashing, are missing
TopSig: Topology Preserving Document Signatures
Performance comparisons between File Signatures and Inverted Files for text
retrieval have previously shown several significant shortcomings of file
signatures relative to inverted files. The inverted file approach underpins
most state-of-the-art search engine algorithms, such as Language and
Probabilistic models. It has been widely accepted that traditional file
signatures are inferior alternatives to inverted files. This paper describes
TopSig, a new approach to the construction of file signatures. Many advances in
semantic hashing and dimensionality reduction have been made in recent times,
but these were not so far linked to general purpose, signature file based,
search engines. This paper introduces a different signature file approach that
builds upon and extends these recent advances. We are able to demonstrate
significant improvements in the performance of signature file based indexing
and retrieval, performance that is comparable to that of state of the art
inverted file based systems, including Language models and BM25. These findings
suggest that file signatures offer a viable alternative to inverted files in
suitable settings and from the theoretical perspective it positions the file
signatures model in the class of Vector Space retrieval models.Comment: 12 pages, 8 figures, CIKM 201
Neural Distributed Autoassociative Memories: A Survey
Introduction. Neural network models of autoassociative, distributed memory
allow storage and retrieval of many items (vectors) where the number of stored
items can exceed the vector dimension (the number of neurons in the network).
This opens the possibility of a sublinear time search (in the number of stored
items) for approximate nearest neighbors among vectors of high dimension. The
purpose of this paper is to review models of autoassociative, distributed
memory that can be naturally implemented by neural networks (mainly with local
learning rules and iterative dynamics based on information locally available to
neurons). Scope. The survey is focused mainly on the networks of Hopfield,
Willshaw and Potts, that have connections between pairs of neurons and operate
on sparse binary vectors. We discuss not only autoassociative memory, but also
the generalization properties of these networks. We also consider neural
networks with higher-order connections and networks with a bipartite graph
structure for non-binary data with linear constraints. Conclusions. In
conclusion we discuss the relations to similarity search, advantages and
drawbacks of these techniques, and topics for further research. An interesting
and still not completely resolved question is whether neural autoassociative
memories can search for approximate nearest neighbors faster than other index
structures for similarity search, in particular for the case of very high
dimensional vectors.Comment: 31 page
Improving the Compact Bit-Sliced Signature Index COBS for Large Scale Genomic Data
In this thesis we investigate the potential for improving the Compact Bit-Sliced Signature Index (COBS) [BBGI19] for large scale genomic data. COBS was developed by Bingmann et al. and is an inverted text index based on Bloom filters. It can be used to index k-mers of DNA samples or q-grams of plain text data and is queried using approximate pattern matching based on the k-mer (or q-gram) profile of a query. In their work Bingmann et al. demonstrated a couple of advantages COBS has over other state of the art approximate k-mer-based indices, some of which are extraordinary fast query and construction times, but as well as the fact that COBS can be constructed and queried even if the index does not fit into main memory. This is one of the reasons we decided to look more closely at some areas we could improve COBS. Our main goal is to make COBS more scalable. Scalability is a very important factor when it comes to handling DNA related data. This is because the amount of sequenced data stored in publicly available archives nearly doubles every year, making it difficult to handle even from the perspective of resources alone. We focus on two main areas in which we try to improve COBS. Those are index compression through clustering and distribution. The thesis presents our findings and improvements achieved in respect to those areas
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