Roaring Bitmap : June 2015 report
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Roaring Bitmaps (January 2016)
Better bitmap performance with Roaring bitmaps
Bitmaps are used to implement fast set operations in software. They are frequently found in databases and search engines.
Without compression, bitmaps scale poorly, so they are often compressed. Many bitmap compression techniques have been proposed, almost all relying primarily on run-length encoding (RLE). For example, Oracle relies on BBC bitmap compression while the version control system Git and Apache Hive rely on EWAH compression.
We can get superior performance with a hybrid compression technique that uses both uncompressed bitmaps and packed arrays inside a two-level tree. An instance of this technique, Roaring, has been adopted by several production platforms (e.g., Apache Lucene/Solr/Elastic, Apache Spark, eBay's Apache Kylin and Metamarkets' Druid).
Overall, our implementation of Roaring can be several times faster (up to two orders of magnitude) than the implementations of traditional RLE-based alternatives (WAH, Concise, EWAH) while compressing better. We review the design choices and optimizations that make these good results possible.
Compressing column-oriented indexes
Column-oriented databases have become fashionable following the work of Stonebraker et al. In the data warehousing industry, the terms "column oriented" and "column store" have become necessary marketing buzzwords. One of the benefits of column-oriented indexes is good compression through run-length encoding (RLE). This type of compression is particularly benefitial since it simultaneously reduce the volume of data and the necessary computations. However, the efficiency of the compression depends on the order of the rows in the table and this is even more important with larger tables. Finding the best row ordering is NP hard. We compare some heuristics for this problem including variations on the lexicographical order, Gray codes, and Hilbert space-filling curves.
Accurate and efficient software microbenchmarks
Software is often improved incrementally. Each software optimization should be assessed with microbenchmarks. In a microbenchmark, we record performance measures such as elapsed time or instruction counts during specific tasks, often in idealized conditions. In principle, the process is easy: if the new code is faster, we adopt it. Unfortunately, there are many pitfalls, such as unrealistic statistical assumptions and poorly designed benchmarks. Abstractions like cloud computing add further challenges. We illustrate effective benchmarking practices with examples.
Fast indexes with roaring #gomtl-10
Presentation on Roaring bitmaps for the Go Montreal meetup (Go 10th anniversary).
Roaring bitmaps are a standard indexing data structure. They are
widely used in search and database engines. For example, Lucene, the
search engine powering Wikipedia relies on Roaring. The Go library
roaring implements Roaring bitmaps in Go. It is used in several
popular systems such as InfluxDB, Pilosa and Bleve. This library is
used in production in several systems, it is part of the Awesome Go
collection. After presenting the library, we will cover some advanced
Go topics such as the use of assembly language, unsafe mappings, and
so forth.
Parsing JSON Really Quickly: Lessons Learned
Our disks and networks can load gigabytes of data per second; we feel strongly that our software should follow suit. Thus we wrote what might be the fastest JSON parser in the world, simdjson. It can parse typical JSON files at speeds of over 2 GB/s on single commodity Intel core with full validation; it is several times faster than conventional parsers.
How did we go so fast? We started with the insight that we should make full use of the SIMD instructions available on commodity processors. These instructions are everywhere, from the ARM chip in your smartphone all to way to server processors. SIMD instructions work on wide registers (e.g., spanning 32 bytes): they are faster because they process more data using fewer instructions. To our knowledge, nobody had ever attempted to produce a full parser for something as complex as JSON by relying primarily on SIMD instructions. And many people were skeptical that a full parser could be done fruitfully with SIMD instructions. We had to develop interesting new strategies that are generally applicable. In the end, we learned several lessons. Maybe one of the most important lesson is the importance of a nearly obsessive focus on performance metrics. We constantly measure the impact of the choices we make.
Next Generation Indexes For Big Data Engineering (ODSC East 2018)
Maximizing performance in data engineering is a daunting challenge. We present some of our work on designing faster indexes, with a particular emphasis on compressed indexes. Some of our prior work includes (1) Roaring indexes which are part of multiple big-data systems such as Spark, Hive, Druid, Atlas, Pinot, Kylin, (2) EWAH indexes are part of Git (GitHub) and included in major Linux distributions.
We will present ongoing and future work on how we can process data faster while supporting the diverse systems found in the cloud (with upcoming ARM processors) and under multiple programming languages (e.g., Java, C++, Go, Python). We seek to minimize shared resources (e.g., RAM) while exploiting algorithms designed for the single-instruction-multiple-data (SIMD) instructions available on commodity processors. Our end goal is to process billions of records per second per core.
The talk will be aimed at programmers who want to better understand the performance characteristics of current big-data systems as well as their evolution. The following specific topics will be addressed:
1. The various types of indexes and their performance characteristics and trade-offs: hashing, sorted arrays, bitsets and so forth.
2. Index and table compression techniques: binary packing, patched coding, dictionary coding, frame-of-reference.
Ingénierie de la performance au sein des mégadonnées
Les index logiciels accélèrent les applications en intelligence d'affaire, en apprentissage machine et en science des données. Ils déterminent souvent la performance des applications portant sur les mégadonnées. Les index efficaces améliorent non seulement la latence et le débit, mais aussi la consommation d'énergie. Plusieurs index font une utilisation parcimonieuse de la mémoire vive afin que les données critiques demeurent près du processeur. Il est aussi souhaitable de travailler directement sur les données compressées afin d'éviter une étape de décodage supplémentaire.
(1) Nous nous intéressons aux index bitmap. Nous les trouvons dans une vaste gamme de systèmes :
Oracle, Hive, Spark, Druid, Kylin, Lucene, Elastic, Git... Ils sont une composante de systèmes, tels que Wikipedia ou GitHub, dont dépendent des millions d'utilisateurs à tous les jours. Nous
présenterons certains progrès récents ayant trait à l'optimisation des index bitmap, tels qu'ils sont utilisés au sein des systèmes actuels. Nous montrons par des exemples comment multiplier la
performance de ces index dans certains cas sur les processeurs bénéficiant d'instructions SIMD (instruction unique, données multiples) avancées.
(2) Nous ciblons aussi les listes d'entiers que l'on trouve au sein des arbres B+, dans les indexes inversés et les index bitmap compressés. Nous donnons un exemple récent de technique de compression (Stream VByte) d’entiers qui permet de décoder des milliards d’entiers compressés par seconde.
SIMD Compression and the Intersection of Sorted Integers
Sorted lists of integers are commonly used in inverted indexes and database systems. They are often compressed in memory. We can use the SIMD instructions available in common processors to boost the speed of integer compression schemes. Our S4-BP128-D4 scheme uses as little as 0.7 CPU cycles per decoded integer while still providing state-of-the-art compression. However, if the subsequent processing of the integers is slow, the effort spent on optimizing decoding speed can be wasted. To show that it does not have to be so, we (1) vectorize and optimize the intersection of posting lists; (2) introduce the SIMD Galloping algorithm. We exploit the fact that one SIMD instruction can compare 4 pairs of integers at once. We experiment with two TREC text collections, GOV2 and ClueWeb09 (Category B), using logs from the TREC million-query track. We show that using only the SIMD instructions ubiquitous in all modern CPUs, our techniques for conjunctive queries can double the speed of a state-of-the-art approach.
Recommended
Roaring Bitmaps (January 2016)
Better bitmap performance with Roaring bitmaps
Bitmaps are used to implement fast set operations in software. They are frequently found in databases and search engines.
Without compression, bitmaps scale poorly, so they are often compressed. Many bitmap compression techniques have been proposed, almost all relying primarily on run-length encoding (RLE). For example, Oracle relies on BBC bitmap compression while the version control system Git and Apache Hive rely on EWAH compression.
We can get superior performance with a hybrid compression technique that uses both uncompressed bitmaps and packed arrays inside a two-level tree. An instance of this technique, Roaring, has been adopted by several production platforms (e.g., Apache Lucene/Solr/Elastic, Apache Spark, eBay's Apache Kylin and Metamarkets' Druid).
Overall, our implementation of Roaring can be several times faster (up to two orders of magnitude) than the implementations of traditional RLE-based alternatives (WAH, Concise, EWAH) while compressing better. We review the design choices and optimizations that make these good results possible.
Compressing column-oriented indexes
Column-oriented databases have become fashionable following the work of Stonebraker et al. In the data warehousing industry, the terms "column oriented" and "column store" have become necessary marketing buzzwords. One of the benefits of column-oriented indexes is good compression through run-length encoding (RLE). This type of compression is particularly benefitial since it simultaneously reduce the volume of data and the necessary computations. However, the efficiency of the compression depends on the order of the rows in the table and this is even more important with larger tables. Finding the best row ordering is NP hard. We compare some heuristics for this problem including variations on the lexicographical order, Gray codes, and Hilbert space-filling curves.
Accurate and efficient software microbenchmarks
Software is often improved incrementally. Each software optimization should be assessed with microbenchmarks. In a microbenchmark, we record performance measures such as elapsed time or instruction counts during specific tasks, often in idealized conditions. In principle, the process is easy: if the new code is faster, we adopt it. Unfortunately, there are many pitfalls, such as unrealistic statistical assumptions and poorly designed benchmarks. Abstractions like cloud computing add further challenges. We illustrate effective benchmarking practices with examples.
Fast indexes with roaring #gomtl-10
Presentation on Roaring bitmaps for the Go Montreal meetup (Go 10th anniversary).
Roaring bitmaps are a standard indexing data structure. They are
widely used in search and database engines. For example, Lucene, the
search engine powering Wikipedia relies on Roaring. The Go library
roaring implements Roaring bitmaps in Go. It is used in several
popular systems such as InfluxDB, Pilosa and Bleve. This library is
used in production in several systems, it is part of the Awesome Go
collection. After presenting the library, we will cover some advanced
Go topics such as the use of assembly language, unsafe mappings, and
so forth.
Parsing JSON Really Quickly: Lessons Learned
Our disks and networks can load gigabytes of data per second; we feel strongly that our software should follow suit. Thus we wrote what might be the fastest JSON parser in the world, simdjson. It can parse typical JSON files at speeds of over 2 GB/s on single commodity Intel core with full validation; it is several times faster than conventional parsers.
How did we go so fast? We started with the insight that we should make full use of the SIMD instructions available on commodity processors. These instructions are everywhere, from the ARM chip in your smartphone all to way to server processors. SIMD instructions work on wide registers (e.g., spanning 32 bytes): they are faster because they process more data using fewer instructions. To our knowledge, nobody had ever attempted to produce a full parser for something as complex as JSON by relying primarily on SIMD instructions. And many people were skeptical that a full parser could be done fruitfully with SIMD instructions. We had to develop interesting new strategies that are generally applicable. In the end, we learned several lessons. Maybe one of the most important lesson is the importance of a nearly obsessive focus on performance metrics. We constantly measure the impact of the choices we make.
Next Generation Indexes For Big Data Engineering (ODSC East 2018)
Maximizing performance in data engineering is a daunting challenge. We present some of our work on designing faster indexes, with a particular emphasis on compressed indexes. Some of our prior work includes (1) Roaring indexes which are part of multiple big-data systems such as Spark, Hive, Druid, Atlas, Pinot, Kylin, (2) EWAH indexes are part of Git (GitHub) and included in major Linux distributions.
We will present ongoing and future work on how we can process data faster while supporting the diverse systems found in the cloud (with upcoming ARM processors) and under multiple programming languages (e.g., Java, C++, Go, Python). We seek to minimize shared resources (e.g., RAM) while exploiting algorithms designed for the single-instruction-multiple-data (SIMD) instructions available on commodity processors. Our end goal is to process billions of records per second per core.
The talk will be aimed at programmers who want to better understand the performance characteristics of current big-data systems as well as their evolution. The following specific topics will be addressed:
1. The various types of indexes and their performance characteristics and trade-offs: hashing, sorted arrays, bitsets and so forth.
2. Index and table compression techniques: binary packing, patched coding, dictionary coding, frame-of-reference.
Ingénierie de la performance au sein des mégadonnées
Les index logiciels accélèrent les applications en intelligence d'affaire, en apprentissage machine et en science des données. Ils déterminent souvent la performance des applications portant sur les mégadonnées. Les index efficaces améliorent non seulement la latence et le débit, mais aussi la consommation d'énergie. Plusieurs index font une utilisation parcimonieuse de la mémoire vive afin que les données critiques demeurent près du processeur. Il est aussi souhaitable de travailler directement sur les données compressées afin d'éviter une étape de décodage supplémentaire.
(1) Nous nous intéressons aux index bitmap. Nous les trouvons dans une vaste gamme de systèmes :
Oracle, Hive, Spark, Druid, Kylin, Lucene, Elastic, Git... Ils sont une composante de systèmes, tels que Wikipedia ou GitHub, dont dépendent des millions d'utilisateurs à tous les jours. Nous
présenterons certains progrès récents ayant trait à l'optimisation des index bitmap, tels qu'ils sont utilisés au sein des systèmes actuels. Nous montrons par des exemples comment multiplier la
performance de ces index dans certains cas sur les processeurs bénéficiant d'instructions SIMD (instruction unique, données multiples) avancées.
(2) Nous ciblons aussi les listes d'entiers que l'on trouve au sein des arbres B+, dans les indexes inversés et les index bitmap compressés. Nous donnons un exemple récent de technique de compression (Stream VByte) d’entiers qui permet de décoder des milliards d’entiers compressés par seconde.
SIMD Compression and the Intersection of Sorted Integers
Sorted lists of integers are commonly used in inverted indexes and database systems. They are often compressed in memory. We can use the SIMD instructions available in common processors to boost the speed of integer compression schemes. Our S4-BP128-D4 scheme uses as little as 0.7 CPU cycles per decoded integer while still providing state-of-the-art compression. However, if the subsequent processing of the integers is slow, the effort spent on optimizing decoding speed can be wasted. To show that it does not have to be so, we (1) vectorize and optimize the intersection of posting lists; (2) introduce the SIMD Galloping algorithm. We exploit the fact that one SIMD instruction can compare 4 pairs of integers at once. We experiment with two TREC text collections, GOV2 and ClueWeb09 (Category B), using logs from the TREC million-query track. We show that using only the SIMD instructions ubiquitous in all modern CPUs, our techniques for conjunctive queries can double the speed of a state-of-the-art approach.
Decoding billions of integers per second through vectorization
In many important applications -- such as search engines and relational database systems -- data is stored in the form of arrays of integers. Encoding and, most importantly, decoding of these arrays consumes considerable CPU time. Therefore, substantial effort has been made to reduce costs associated with compression and decompression. In particular, researchers have exploited the superscalar nature of modern processors and SIMD instructions. Nevertheless, we introduce a novel vectorized scheme called SIMD-BP128 that improves over previously proposed vectorized approaches. It is nearly twice as fast as the previously fastest schemes on desktop processors (varint-G8IU and PFOR). At the same time, SIMD-BP128 saves up to 2 bits per integer. For even better compression, we propose another new vectorized scheme (SIMD-FastPFOR) that has a compression ratio within 10% of a state-of-the-art scheme (Simple-8b) while being two times faster during decoding.
Logarithmic Discrete Wavelet Transform for High-Quality Medical Image Compres...
Nowadays, medical image compression is an essential process in eHealth systems. Compressing medical images in high quality is a vital demand to avoid misdiagnosing medical exams by radiologists. WAAVES is a promising medical images compression algorithm based on the discrete wavelet transform (DWT) that achieves a high compression performance compared to the state of the art. The main aims of this work are to enhance image quality when compressing using WAAVES and to provide a high-speed DWT architecture for image compression on embedded systems. Regarding the quality improvement, the logarithmic number systems (LNS) was explored to be used as an alternative to the linear arithmetic in DWT computations. A new LNS library was developed and validated to realize the logarithmic DWT. In addition, a new quantization method called (LNS-Q) based on logarithmic arithmetic was proposed. A novel compression scheme (LNS-WAAVES) based on integrating the Hybrid-DWT and the LNS-Q method with WAAVES was developed. Hybrid-DWT combines the advantages of both the logarithmic and the linear domains leading to enhancement of the image quality and the compression ratio. The results showed that LNS-WAAVES is able to achieve an improvement in the quality by a percentage of 8% and up to 34% compared to WAAVES depending on the compression configuration parameters and the image modalities.
Engineering fast indexes (Deepdive)
Contemporary computing hardware offers massive new performance opportunities. Yet high-performance programming remains a daunting challenge.
Engineering fast indexes
Contemporary computing hardware offers massive new performance opportunities. Yet high-performance programming remains a daunting challenge.
We present some of the lessons learned while designing faster indexes, with a particular emphasis on compressed bitmap indexes. Compressed bitmap indexes accelerate queries in popular systems such as Apache Spark, Git, Elastic, Druid and Apache Kylin.
MaskedVByte: SIMD-accelerated VByte
We consider the ubiquitous technique of VByte compression, which represents each integer as a variable length sequence of bytes. The low 7 bits of each byte encode a portion of the integer, and the high bit of each byte is reserved as a continuation flag. This flag is set to 1 for all bytes except the last, and the decoding of each integer is complete when a byte with a high bit of 0 is encountered. VByte decoding can be a performance bottleneck especially when the unpredictable lengths of the encoded integers cause frequent branch mispredictions. Previous attempts to accelerate VByte decoding using SIMD vector instructions have been disappointing, prodding search engines such as Google to use more complicated but faster-to-decode formats for performance-critical code. Our decoder (Masked VByte) is 2 to 4 times faster than a conventional scalar VByte decoder, making the format once again competitive with regard to speed.
Jeff Plaisance, Nathan Kurz, Daniel Lemire, Vectorized VByte Decoding, International Symposium on Web Algorithms 2015, 2015.
http://arxiv.org/pdf/1503.07387.pdf
La vectorisation des algorithmes de compression
Depuis la mise en marché du Pentium 4, nos processeurs bénéficient d'instructions vectorielles. En tenant compte explicitement de ces instructions dans la conception de nos algorithmes, nous pouvons grandement accélérer les calculs. À titre d'exemple, considérons la compression des listes d'entiers telle qu'elle s'effectue au sein de la plupart des moteurs de recherche ou des bases de données. En cette matière, nous utilisons souvent encore des algorithmes développés dans les années 70. Nous expliquerons comment on peut faire beaucoup mieux
en ce qui a trait à la vitesse en exploitant les instructions
vectorielles.
Decoding billions of integers per second through vectorization
In many important applications -- such as search engines and relational database systems -- data is stored in the form of arrays of integers. Encoding and, most importantly, decoding of these arrays consumes considerable CPU time. Therefore, substantial effort has been made to reduce costs associated with compression and decompression. In particular, researchers have exploited the superscalar nature of modern processors and SIMD instructions. Nevertheless, we introduce a novel vectorized scheme called SIMD-BP128 that improves over previously proposed vectorized approaches. It is nearly twice as fast as the previously fastest schemes on desktop processors (varint-G8IU and PFOR). At the same time, SIMD-BP128 saves up to 2 bits per integer. For even better compression, we propose another new vectorized scheme (SIMD-FastPFOR) that has a compression rate within 10% of a state-of-the-art scheme (Simple-8b) while being two times faster during decoding.
Extracting, Transforming and Archiving Scientific Data
It is becoming common to archive research datasets that are not only large but also numerous. In addition, their corresponding metadata and the software required to analyse or display them need to be archived. Yet the manual curation of research data can be difficult and expensive, particularly in very large digital repositories, hence the importance of models and tools for automating digital curation tasks. The automation of these tasks faces three major challenges: (1) research data and data sources are highly heterogeneous, (2) future research needs are difficult to anticipate, (3) data is hard to index. To address these problems, we propose the Extract, Transform and Archive (ETA) model for managing and mechanizing the curation of research data. Specifically, we propose a scalable strategy for addressing the research-data problem, ranging from the extraction of legacy data to its long-term storage. We review some existing solutions and propose novel avenues of research.
Innovation without permission: from Codd to NoSQL
Practitioners often fail to apply textbook database design principles. We observe both a perversion of the relational model and a growth of less formal alternatives. Overall, there is an opposition between the analytic thought that prevailed when many data modeling techniques were initiated, and the pragmatism which now dominates among practitioners. There are at least two recent trends supporting this rejection of traditional models:
(1) the rise of the sophisticated user,
most notably in social media is challenge to the rationalist view, as it blurs the distinction between design and operation,
(2) in the new technological landscape where there are billions of interconnected computers worldwide, simple concepts like
consistency sometimes become prohibitively expensive. Overall, for a wide range of information systems, design and operation are becoming integrated in the spirit of pragmatism. Thus, we are left with design methodologies which embrace fast and continual iterations and and exploratory testing. These methodologies allow innovation without permission in that the right to design new features is no longer so closely guarded.
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Write good papers
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Faster Column-Oriented Indexes
Recent research results in optimizing column-oriented indexes for faster data warehousing. This talks aims to answer the following question: when is sorting the table a sufficiently good optimization?
All About Bitmap Indexes... And Sorting Them
A review of bitmap index from an academic perspective. Several theoretical results are presented. The talk also discuss technical issues regarding sorting the tables prior to indexing, as a way to improve the indexes.
Much of the talk is based on the following preprint:
Daniel Lemire, Owen Kaser, Kamel Aouiche, Sorting improves word-aligned bitmap indexes.
http://arxiv.org/abs/0901.3751
A Comparison of Five Probabilistic View-Size Estimation Techniques in OLAP
A data warehouse cannot materialize all possible views, hence we must estimate quickly, accurately, and reliably the size of views to determine the best candidates for materialization. Many available techniques for view-size estimation make particular statistical assumptions and their error can be large. Comparatively, unassuming probabilistic techniques are slower, but they estimate accurately and reliability very large view sizes using little memory. We compare five unassuming hashing-based view-size estimation techniques including Stochastic Probabilistic Counting and LogLog Probabilistic Counting. Our experiments show that only Generalized Counting, Gibbons-Tirthapura, and Adaptive Counting provide universally tight estimates irrespective of the size of the view; of those, only Adaptive Counting remains constantly fast as we increase the memory budget.
Tag-Cloud Drawing: Algorithms for Cloud Visualization
Tag clouds provide an aggregate of tag-usage statistics. They are typically sent as in-line HTML to browsers. However, display mechanisms suited for ordinary text are not ideal for tags, because font sizes may vary widely on a line. As well, the typical layout does not account for relationships that may be known between tags. This paper presents models and algorithms to improve the display of tag clouds that con- sist of in-line HTML, as well as algorithms that use nested tables to achieve a more general 2-dimensional layout in which tag relationships are considered. The first algorithms leverage prior work in typesetting and rectangle packing, whereas the second group of algorithms leverage prior work in Electronic Design Automation. Experiments show our algorithms can be efficiently implemented and perform well.
Histogram-Aware Sorting for Enhanced Word-Aligned Compression in Bitmap Indexes
Bitmap indexes must be compressed to reduce input/output costs and minimize CPU usage. To accelerate logical operations (AND, OR, XOR) over bitmaps, we use techniques based on run-length encoding (RLE), such as Word-Aligned Hybrid (WAH) compression. These techniques are sensitive to the order of the rows: a simple lexicographical sort can divide the index size by 9 and make indexes several times faster. We investigate reordering heuristics based on computed attribute-value histograms. Simply permuting the columns of the table based on these histograms can increase the sorting efficiency by 40%.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Connector Corner: Automate dynamic content and events by pushing a button
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
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Akshay Agnihotri, Product Manager
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"Impact of front-end architecture on development cost", Viktor Turskyi
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
State of ICS and IoT Cyber Threat Landscape Report 2024 preview
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
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In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
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Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
More Related Content
More from Daniel Lemire
Decoding billions of integers per second through vectorization
In many important applications -- such as search engines and relational database systems -- data is stored in the form of arrays of integers. Encoding and, most importantly, decoding of these arrays consumes considerable CPU time. Therefore, substantial effort has been made to reduce costs associated with compression and decompression. In particular, researchers have exploited the superscalar nature of modern processors and SIMD instructions. Nevertheless, we introduce a novel vectorized scheme called SIMD-BP128 that improves over previously proposed vectorized approaches. It is nearly twice as fast as the previously fastest schemes on desktop processors (varint-G8IU and PFOR). At the same time, SIMD-BP128 saves up to 2 bits per integer. For even better compression, we propose another new vectorized scheme (SIMD-FastPFOR) that has a compression ratio within 10% of a state-of-the-art scheme (Simple-8b) while being two times faster during decoding.
Logarithmic Discrete Wavelet Transform for High-Quality Medical Image Compres...
Nowadays, medical image compression is an essential process in eHealth systems. Compressing medical images in high quality is a vital demand to avoid misdiagnosing medical exams by radiologists. WAAVES is a promising medical images compression algorithm based on the discrete wavelet transform (DWT) that achieves a high compression performance compared to the state of the art. The main aims of this work are to enhance image quality when compressing using WAAVES and to provide a high-speed DWT architecture for image compression on embedded systems. Regarding the quality improvement, the logarithmic number systems (LNS) was explored to be used as an alternative to the linear arithmetic in DWT computations. A new LNS library was developed and validated to realize the logarithmic DWT. In addition, a new quantization method called (LNS-Q) based on logarithmic arithmetic was proposed. A novel compression scheme (LNS-WAAVES) based on integrating the Hybrid-DWT and the LNS-Q method with WAAVES was developed. Hybrid-DWT combines the advantages of both the logarithmic and the linear domains leading to enhancement of the image quality and the compression ratio. The results showed that LNS-WAAVES is able to achieve an improvement in the quality by a percentage of 8% and up to 34% compared to WAAVES depending on the compression configuration parameters and the image modalities.
Engineering fast indexes (Deepdive)
Contemporary computing hardware offers massive new performance opportunities. Yet high-performance programming remains a daunting challenge.
Engineering fast indexes
Contemporary computing hardware offers massive new performance opportunities. Yet high-performance programming remains a daunting challenge.
We present some of the lessons learned while designing faster indexes, with a particular emphasis on compressed bitmap indexes. Compressed bitmap indexes accelerate queries in popular systems such as Apache Spark, Git, Elastic, Druid and Apache Kylin.
MaskedVByte: SIMD-accelerated VByte
We consider the ubiquitous technique of VByte compression, which represents each integer as a variable length sequence of bytes. The low 7 bits of each byte encode a portion of the integer, and the high bit of each byte is reserved as a continuation flag. This flag is set to 1 for all bytes except the last, and the decoding of each integer is complete when a byte with a high bit of 0 is encountered. VByte decoding can be a performance bottleneck especially when the unpredictable lengths of the encoded integers cause frequent branch mispredictions. Previous attempts to accelerate VByte decoding using SIMD vector instructions have been disappointing, prodding search engines such as Google to use more complicated but faster-to-decode formats for performance-critical code. Our decoder (Masked VByte) is 2 to 4 times faster than a conventional scalar VByte decoder, making the format once again competitive with regard to speed.
Jeff Plaisance, Nathan Kurz, Daniel Lemire, Vectorized VByte Decoding, International Symposium on Web Algorithms 2015, 2015.
http://arxiv.org/pdf/1503.07387.pdf
La vectorisation des algorithmes de compression
Depuis la mise en marché du Pentium 4, nos processeurs bénéficient d'instructions vectorielles. En tenant compte explicitement de ces instructions dans la conception de nos algorithmes, nous pouvons grandement accélérer les calculs. À titre d'exemple, considérons la compression des listes d'entiers telle qu'elle s'effectue au sein de la plupart des moteurs de recherche ou des bases de données. En cette matière, nous utilisons souvent encore des algorithmes développés dans les années 70. Nous expliquerons comment on peut faire beaucoup mieux
en ce qui a trait à la vitesse en exploitant les instructions
vectorielles.
Decoding billions of integers per second through vectorization
In many important applications -- such as search engines and relational database systems -- data is stored in the form of arrays of integers. Encoding and, most importantly, decoding of these arrays consumes considerable CPU time. Therefore, substantial effort has been made to reduce costs associated with compression and decompression. In particular, researchers have exploited the superscalar nature of modern processors and SIMD instructions. Nevertheless, we introduce a novel vectorized scheme called SIMD-BP128 that improves over previously proposed vectorized approaches. It is nearly twice as fast as the previously fastest schemes on desktop processors (varint-G8IU and PFOR). At the same time, SIMD-BP128 saves up to 2 bits per integer. For even better compression, we propose another new vectorized scheme (SIMD-FastPFOR) that has a compression rate within 10% of a state-of-the-art scheme (Simple-8b) while being two times faster during decoding.
Extracting, Transforming and Archiving Scientific Data
It is becoming common to archive research datasets that are not only large but also numerous. In addition, their corresponding metadata and the software required to analyse or display them need to be archived. Yet the manual curation of research data can be difficult and expensive, particularly in very large digital repositories, hence the importance of models and tools for automating digital curation tasks. The automation of these tasks faces three major challenges: (1) research data and data sources are highly heterogeneous, (2) future research needs are difficult to anticipate, (3) data is hard to index. To address these problems, we propose the Extract, Transform and Archive (ETA) model for managing and mechanizing the curation of research data. Specifically, we propose a scalable strategy for addressing the research-data problem, ranging from the extraction of legacy data to its long-term storage. We review some existing solutions and propose novel avenues of research.
Innovation without permission: from Codd to NoSQL
Practitioners often fail to apply textbook database design principles. We observe both a perversion of the relational model and a growth of less formal alternatives. Overall, there is an opposition between the analytic thought that prevailed when many data modeling techniques were initiated, and the pragmatism which now dominates among practitioners. There are at least two recent trends supporting this rejection of traditional models:
(1) the rise of the sophisticated user,
most notably in social media is challenge to the rationalist view, as it blurs the distinction between design and operation,
(2) in the new technological landscape where there are billions of interconnected computers worldwide, simple concepts like
consistency sometimes become prohibitively expensive. Overall, for a wide range of information systems, design and operation are becoming integrated in the spirit of pragmatism. Thus, we are left with design methodologies which embrace fast and continual iterations and and exploratory testing. These methodologies allow innovation without permission in that the right to design new features is no longer so closely guarded.
Fo
Write good papers
How to write good research papers. This talk covers everything from selecting a good title, writing a good abstract, crafting good figures, and so on.
Faster Column-Oriented Indexes
Recent research results in optimizing column-oriented indexes for faster data warehousing. This talks aims to answer the following question: when is sorting the table a sufficiently good optimization?
All About Bitmap Indexes... And Sorting Them
A review of bitmap index from an academic perspective. Several theoretical results are presented. The talk also discuss technical issues regarding sorting the tables prior to indexing, as a way to improve the indexes.
Much of the talk is based on the following preprint:
Daniel Lemire, Owen Kaser, Kamel Aouiche, Sorting improves word-aligned bitmap indexes.
http://arxiv.org/abs/0901.3751
A Comparison of Five Probabilistic View-Size Estimation Techniques in OLAP
A data warehouse cannot materialize all possible views, hence we must estimate quickly, accurately, and reliably the size of views to determine the best candidates for materialization. Many available techniques for view-size estimation make particular statistical assumptions and their error can be large. Comparatively, unassuming probabilistic techniques are slower, but they estimate accurately and reliability very large view sizes using little memory. We compare five unassuming hashing-based view-size estimation techniques including Stochastic Probabilistic Counting and LogLog Probabilistic Counting. Our experiments show that only Generalized Counting, Gibbons-Tirthapura, and Adaptive Counting provide universally tight estimates irrespective of the size of the view; of those, only Adaptive Counting remains constantly fast as we increase the memory budget.
Tag-Cloud Drawing: Algorithms for Cloud Visualization
Tag clouds provide an aggregate of tag-usage statistics. They are typically sent as in-line HTML to browsers. However, display mechanisms suited for ordinary text are not ideal for tags, because font sizes may vary widely on a line. As well, the typical layout does not account for relationships that may be known between tags. This paper presents models and algorithms to improve the display of tag clouds that con- sist of in-line HTML, as well as algorithms that use nested tables to achieve a more general 2-dimensional layout in which tag relationships are considered. The first algorithms leverage prior work in typesetting and rectangle packing, whereas the second group of algorithms leverage prior work in Electronic Design Automation. Experiments show our algorithms can be efficiently implemented and perform well.
Histogram-Aware Sorting for Enhanced Word-Aligned Compression in Bitmap Indexes
Bitmap indexes must be compressed to reduce input/output costs and minimize CPU usage. To accelerate logical operations (AND, OR, XOR) over bitmaps, we use techniques based on run-length encoding (RLE), such as Word-Aligned Hybrid (WAH) compression. These techniques are sensitive to the order of the rows: a simple lexicographical sort can divide the index size by 9 and make indexes several times faster. We investigate reordering heuristics based on computed attribute-value histograms. Simply permuting the columns of the table based on these histograms can increase the sorting efficiency by 40%.
More from Daniel Lemire (16)
Decoding billions of integers per second through vectorization
Decoding billions of integers per second through vectorization
Logarithmic Discrete Wavelet Transform for High-Quality Medical Image Compres...
Logarithmic Discrete Wavelet Transform for High-Quality Medical Image Compres...
Decoding billions of integers per second through vectorization
Decoding billions of integers per second through vectorization
Extracting, Transforming and Archiving Scientific Data
Extracting, Transforming and Archiving Scientific Data
A Comparison of Five Probabilistic View-Size Estimation Techniques in OLAP
A Comparison of Five Probabilistic View-Size Estimation Techniques in OLAP
Tag-Cloud Drawing: Algorithms for Cloud Visualization
Tag-Cloud Drawing: Algorithms for Cloud Visualization
Histogram-Aware Sorting for Enhanced Word-Aligned Compression in Bitmap Indexes
Histogram-Aware Sorting for Enhanced Word-Aligned Compression in Bitmap Indexes
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Connector Corner: Automate dynamic content and events by pushing a button
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I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
State of ICS and IoT Cyber Threat Landscape Report 2024 preview
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
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Cyber risk predictions
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In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
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Learn about:
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The Art of the Pitch: WordPress Relationships and Sales
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Search and Society: Reimagining Information Access for Radical Futures
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UiPath Test Automation using UiPath Test Suite series, part 3
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UI automation Introduction,
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Desktop automation flow
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The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
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Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
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Neuro-symbolic is not enough, we need neuro-*semantic*
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
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Neuro-symbolic is not enough, we need neuro-*semantic*
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