jm + bitmaps   6

qp tries: smaller and faster than crit-bit tries
interesting new data structure from Tony Finch. "Some simple benchmarks say qp tries have about 1/3 less memory overhead and are about 10% faster than crit-bit tries."
crit-bit  popcount  bits  bitmaps  tries  data-structures  via:fanf  qp-tries  crit-bit-tries  hacks  memory 
october 2015 by jm
A simple guide to 9-patch for Android UI
This is a nifty hack. TIL!

'9-patch uses png transparency to do an advanced form of 9-slice or scale9. The guides are straight, 1-pixel black lines drawn on the edge of your image that define the scaling and fill of your image. By naming your image file name.9.png, Android will recognize the 9.png format and use the black guides to scale and fill your bitmaps.'
android  design  9-patch  scaling  images  bitmaps  scale9  9-slice  ui  graphics 
july 2015 by jm
Roaring Bitmaps
Bitsets, also called bitmaps, are commonly used as fast data structures. Unfortunately, they can use too much memory. To compensate, we often use compressed bitmaps. Roaring bitmaps are compressed bitmaps which tend to outperform conventional compressed bitmaps such as WAH, EWAH or Concise. In some instances, they can be hundreds of times faster and they often offer significantly better compression.

Roaring bitmaps are used in Apache Lucene (as of version 5.0 using an independent implementation) and Apache Spark (as of version 1.2).
bitmaps  bitsets  sets  data-structures  bits  compression  lucene  spark  daniel-lemire  algorithms 
november 2014 by jm
javaewah
The bit array data structure is implemented in Java as the BitSet class. Unfortunately, this fails to scale without compression. JavaEWAH is a word-aligned compressed variant of the Java bitset class. It uses a 64-bit run-length encoding (RLE) compression scheme. We trade-off some compression for better processing speed. We also have a 32-bit version which compresses better, but is not as fast.

In general, the goal of word-aligned compression is not to achieve the best compression, but rather to improve query processing time. Hence, we try to save CPU cycles, maybe at the expense of storage. However, the EWAH scheme we implemented is always more efficient storage-wise than an uncompressed bitmap (as implemented in the BitSet class). Unlike some alternatives, javaewah does not rely on a patented scheme.
javaewah  wah  rle  compression  bitmaps  bitmap-indexes  bitset  algorithms  data-structures 
april 2013 by jm
FastBit: An Efficient Compressed Bitmap Index Technology
an [LGPL] open-source data processing library following the spirit of NoSQL movement. It offers a set of searching functions supported by compressed bitmap indexes. It treats user data in the column-oriented manner similar to well-known database management systems such as Sybase IQ, MonetDB, and Vertica. It is designed to accelerate user's data selection tasks without imposing undue requirements. In particular, the user data is NOT required to be under the control of FastBit software, which allows the user to continue to use their existing data analysis tools.

The key technology underlying the FastBit software is a set of compressed bitmap indexes. In database systems, an index is a data structure to accelerate data accesses and reduce the query response time. Most of the commonly used indexes are variants of the B-tree, such as B+-tree and B*-tree. FastBit implements a set of alternative indexes called compressed bitmap indexes. Compared with B-tree variants, these indexes provide very efficient searching and retrieval operations, but are somewhat slower to update after a modification of an individual record.

A key innovation in FastBit is the Word-Aligned Hybrid compression (WAH) for the bitmaps.[...] Another innovation in FastBit is the multi-level bitmap encoding methods.
fastbit  nosql  algorithms  indexing  search  compressed-bitmaps  indexes  wah  bitmaps  compression 
april 2013 by jm

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