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| （相同用户的47个中间修订版本未显示） |
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| − | = 索引 = | + | =索引子系统= |
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| − | *索引（Index）
| + | 索引子系统（Index-subsystem）是[[大数据系统]]的核心组件（Component）。 |
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| − | 索引是加快查找的数据结构（Data Structure）。用于索引功能的数据结构有哈希表（Hash Table）、树（Tree）、整数数组/链表（Integer Array/Linked List）和位图（Bitmap）。
| + | NoSQL引擎主要组件是索引，或者是面向特定应用的索引结构。 |
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| − | 可参考《数据结构》教科书中的搜索（Search）的章节。
| + | "NoSQL engines are (basically) just indexes!" |
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| − | * 教科书：
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| − | # Thomas H. Cormen, Introduction to algorithms, third edition, MIT press, 2009.
| + | 参见[[索引]] |
| − | # Eva Tardos and Jon Kleinberg, Algorithm Design, Pearson, 2006.
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| − | =索引的形式=
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| − | 索引的数据结构包括哈希表（Hash Table）、树（Tree）、整数数组/链表（Integer Array/ Linked List）和位图（Bitmap）。
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| − | == 哈希表（Hash Table） ==
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| − | *[http://billmill.org/bloomfilter-tutorial BloomFilter]
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| − | *[https://github.com/magnuss/java-bloomfilter Java-Bloomfilter]
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| − | *[https://github.com/lemire/bloofi Bloofi]
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| − | **研究论文：
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| − | # B. H. Bloom,Space/time trade-offs in hash coding with allowable errors, Commun. ACM, Volume 13 Issue 7, Pages 422-426, July 1970.
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| − | # Crainiceanu, Adina, and Daniel Lemire. "Bloofi: Multidimensional Bloom Filters." Information Systems 54 (2015): 311-324.
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| − | == 树（Tree） ==
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| − | * [https://en.wikipedia.org/wiki/B-tree B-tree]
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| − | * [https://en.wikipedia.org/wiki/K-d_tree ''k''-d tree]
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| − | * [https://en.wikipedia.org/wiki/R-tree R-tree]
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| − | * [https://en.wikipedia.org/wiki/R%2B_tree R+ tree]
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| − | **研究论文：
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| − | # Guttman, Antonin. R-trees: a dynamic index structure for spatial searching. Vol. 14, no. 2. ACM, 1984.
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| − | # Sellis, Timos, Nick Roussopoulos, and Christos Faloutsos. "The R+-Tree: A Dynamic Index for Multi-Dimensional Objects." VLDB, 1987.
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| − | == 整数数组/链表（Integer Array/List） ==
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| − | Verbatim Integer List 带来的空间开销，人们发明了Integer List Compression机制，尤其是compressing 32-bit unsigned integers。
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| − | * BP128 (Binary packing ) / SIMDBP128 /
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| − | * [https://github.com/lemire/FastPFor FastPFor]
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| − | * FOR (Frame of reference) / PFOR (Patched Frame of reference) / PFOR Delta (PFOR on deltas) / NewPforDelta /SIMDPforDelta
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| − | * Simple16 / Simple8b
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| − | * VByte（or VB ） / VarIntGB（or GroupVB ） / StreamVByte /MaskedVByte [http://maskedvbyte.org/ SIMD-accelerated VByte] / [https://github.com/lemire/MaskedVByte MaskedVByte]
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| − | * PEF (Partitioned Elias-Fano) [http://github.com/ot/partitioned_elias_fano Partitioned Elias-Fano]
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| − | * Data Structures for Inverted Indexes ([https://github.com/ot/ds2i ds2i])
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| − | **研究论文：
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| − | # Daniel Lemire and Christoph Rupp. Upscaledb: Efficient Integer-Key Compression in a Key-Value Store using SIMD Instructions. Information Systems, 2017.
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| − | # D. Lemire, L. Boytsov, N. Kurz, SIMD compression and the intersection of sorted integers, Softw. Pract. Exp. 46 (6) (2016) 723–749.
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| − | # Jeff Plaisance, Nathan Kurz, and Daniel Lemire. "Vectorized vbyte decoding." CoRR, abs/1503.07387, 2015.
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| − | # Jeff Plaisance, Nathan Kurz, Daniel Lemire, Vectorized VByte Decoding, International Symposium on Web Algorithms 2015, 2015.
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| − | # D. Lemire and L. Boytsov. Decoding billions of integers per second through vectorization. Softw. Pract. Exp. 45 (1) (2015) 1–29.
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| − | # Inoue, Hiroshi, Moriyoshi Ohara, and Kenjiro Taura, Faster Set Intersection with SIMD instructions by Reducing Branch Mispredictions, VLDB 2014.
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| − | # Kane, Andrew, and Frank Wm Tompa, Skewed Partial Bitvectors for List Intersection, SIGIR 2014.
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| − | # Schlegel, Benjamin, Thomas Willhalm, and Wolfgang Lehner. Fast Sorted-Set Intersection using SIMD Instructions, ADMS 2011.
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| − | # Zukowski, Marcin, Sandor Heman, Niels Nes, and Peter Boncz. "Super-scalar RAM-CPU cache compression." ICDE 2006.
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| − | == 位图（Bitmap） ==
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| − | ===Bit array ===
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| − | A bit array (also known as bit map , bit set, bit string, or bit vector) is an array data structure that compactly stores bits. It can be used to implement a simple set data structure.
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| − | https://en.wikipedia.org/wiki/Bit_array
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| − | * Population / Hamming weight
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| − | * Find first set (ffs) or Find first one
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| − | * Inversion
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| − | **研究论文：
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| − | # Matthias Vallentin, Vern Paxson, and Robin Sommer. "VAST: a unified platform for interactive network forensics." NSDI 2016.
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| − | # Gonzalo Navarro and Eliana Providel. “Fast, Small, Simple Rank/Select on Bitmaps.” SEA 2013.
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| − | ===位图压缩===
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| − | Verbatim Bitmap 带来的空间开销，尤其是bitmap很稀疏的情况下。为此，人们发明了Bitmap Compression机制。不仅可以降低空间开销，而且可以显著加快运算速度。
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| − | **可运算的位图压缩方法
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| − | * BBC (Byte-aligned Bitmap Compression)
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| − | * WAH (Word Aligned Hybrid)
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| − | * PLWAH (Position List Word-Aligned Hybrid)
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| − | * EWAH (Enhanced Word-Aligned Hybrid )
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| − | * CONCISE (COmpressed N Composable Integer SEt)
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| − | * PWAH (Partitioned Word-Aligned Hybrid)
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| − | * COMPAX (COMPressed Adaptive indeX )
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| − | * SBH （Super byte-aligned hybrid）
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| − | ** 研究论文
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| − | [1] Antoshekov G. Byte-aligned bitmap compression. Proc of the Conf on Data Compression. Piscataway, NJ: IEEE, 1994: 363- 098, 1994.
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| − | [2] Wu, Kesheng, Ekow J. Otoo, and Arie Shoshani. "Compressing bitmap indexes for faster search operations." In Scientific and Statistical Database Management, 2002. Proceedings. 14th International Conference on, pp. 99-108. IEEE, 2002.
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| − | [3] Wu, K., Otoo, E. J., and Shoshani, A. Optimizing bitmap indices with efficient compression. ACM Transactions on Database Systems (TODS), 31(1), 1-38, 2006.
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| − | [4] Stabno, Michał, and Robert Wrembel. "RLH: Bitmap compression technique based on run-length and Huffman encoding." Information Systems 34, no. 4, pp.400-414, 2009.
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| − | [5] F. Fusco, M. P. Stoecklin, and M. Vlachos, “Net-fli: on-the-fly compression, archiving and indexing of streaming network traffic,” Proceedings of the VLDB Endowment, vol. 3, no. 1-2, pp. 1382–1393, 2010.
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| − | [6] F. Deli`ege, T. B. Pedersen. Position list word aligned hybrid: optimizing space and performance for compressed bitmaps. In Proceeding of the 13th International Conference on Extending Database Technology, 2010.
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| − | [7] F. Corrales, D. Chiu, and J. Sawin, “Variable Length Compression for Bitmap Indexes,” DEXA’11, Springer-Verlag, pp.381-395, 2011.
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| − | [8] D. Lemire et al., “Sorting improves word-aligned bitmap indexes,” Data & Knowledge Engineering, 69(1), pp.3-28, 2010.
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| − | [9] S. J. van Schaik et al., “A memory efficient reachability data structure through bit vector compression,” SIGMOD, pp.913-924, 2011.
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| − | [10] S. Chambi, D. Lemire, O. Kaser, and R. Godin, “Better bitmap performance with roaring bitmaps,” Software: Practice and Experience, 2015.
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| − | [11] A. Schmidt, D. Kimmig, and M. Beine, “DFWAH: A Proposal of a New Compression Scheme of Medium-Sparse Bitmaps,” in the Third International Conference on Advances in Databases, Knowledge, and Data Applications (DBKDA 2011), pp. 192-195.
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| − | [12] R. Slechta, J. Sawin, B. McCamish, D. Chiu and G. Canahuate, A tunable compression framework for bitmap indices, In Data Engineering (ICDE’ 2014), pp. 484-495. IEEE.
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| − | **研究专利
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| − | [1] BBC, Byte aligned data compression, DEC/Oracle, http://www.google.com/patents/US5363098.
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| − | [2] WAH, Word aligned bitmap compression method, data structure, and apparatus, UC Berkeley, http://www.google.com/patents/US6831575.
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| − | [3] COMPAX, Network analysis, IBM, http://www.google.com/patents/US20120054160.
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| − | [4] PLWAH, Algorhyme, http://www.google.com/patents/US9236881.
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| − | == 混合结构（Hybrid） ==
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| − | 融合几种独立结构的混合结构。
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| − | === Roaring ===
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| − | * Roaring Bitmap
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| − | Roaring Bitmap 是一种为索引（Indexes）的运算(与，或，非)而优化的混合数据结构。
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| − | Roaring 数据结构内部采用位图Bitmap，整数数组Arrays和游程编码Runs的表示形式，三种表示形式是平等的。当数据以其中某种表示结构的表示时，整体空间为最优，Roaring则采用该表示结构。也就是说，Roaring采用空间最优化的数据表示结构。
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| − | Roaring表示的索引之间可以进行高效的运算。运算时，无非也是位图Bitmap，整数数组Arrays和游程编码Runs这三种结构之间的运算。
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| − | 所有，Roaring是兼顾空间与时间效率的一种有效的索引结构。
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| − | Roaring网址：http://roaringbitmap.org/
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| − | Roaring的Java实现 [https://github.com/RoaringBitmap/RoaringBitmap Java-Roaring]
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| − | Roaring的C/C++实现 [https://github.com/RoaringBitmap/CRoaring CRoaring]
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| − | * Roaring 研究论文：
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| − | # Lemire, Daniel, Gregory Ssi‐Yan‐Kai, and Owen Kaser. "Consistently faster and smaller compressed bitmaps with Roaring." Software: Practice and Experience 46.11 (2016): 1547-1569.
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| − | # Samy Chambi, Daniel Lemire, Owen Kaser, and Robert Godin. "Better bitmap performance with Roaring bitmaps." Software: practice and experience, 2015.
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| − | ===其它混合结构===
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| − | # Athanassoulis, Manos, and Anastasia Ailamaki. "BF-tree: approximate tree indexing." Proceedings of the VLDB Endowment 7, no. 14 (2014): 1881-1892.
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| − | # Lakshminarasimhan, Sriram, et al. "Scalable in situ scientific data encoding for analytical query processing." HPDC 2013.
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| − | **研究论文：
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| − | #Juha Kärkkäinen, Dominik Kempa, Simon J. Puglisi. Hybrid Compression of Bitvectors for the FM-Index. In Proc. 2014 Data Compression Conference (DCC 2014), pp. 302-311, 2014.
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| − | #Gagie, Travis, Gonzalo Navarro, and Simon J. Puglisi. 2012. “New algorithms on wavelet trees and applications to information retrieval.” Theoretical Computer Science 426: 25–41.
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| − | == 其它结构（Others） ==
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| − | === SDSL - Succinct Data Structure Library ===
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| − | *[https://github.com/simongog/sdsl-lite Succinct Data Structure Library]
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| − | *[https://github.com/simongog/sdsl-lite/wiki/List-of-Implemented-Data-Structures List of Implemented Data Structures]
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| − | ** Bitvectors supporting Rank and Select
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| − | ** Integer Vectors
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| − | ** Wavelet Trees
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| − | ** Compressed Suffix Arrays (CSA)
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| − | ** Balanced Parentheses Representations
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| − | ** Longest Common Prefix (LCP) Arrays
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| − | ** Compressed Suffix Trees (CST)
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| − | ** Range Minimum/Maximum Query (RMQ) Structures
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| − | =索引（大数据）=
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| − | 大数据系统的数据管理，采用位图索引、树索引等。
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| − | ===本组研究 ===
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| − | *研究定位
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| − | 数据分析层面：关联规则分析（功能）（系统级）
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| − | 算法实现层面： Apriori 算法 （进程级）
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| − | 数据结构层面：Bitmap （C++/Go/函数库）（编程语言）
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| − | 处理器执行层面：SSE/AVX/AVX2/FMA （机器指令 Instructions）
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| − | *本组工作：
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| − | **CODIS （Compressing Dirty Snippet） （C++ ）
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| − | **[http://gitlab.icenter.tsinghua.edu.cn/zhenchen/BAH BAH]（Byte Aligned Hybrid） （C++ Language）
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| − | **[https://github.com/thuwuyinjun/CAMP CAMP]（Common Affix Merging with Partition） （Java Language）
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| − | **SPLWAH （CUDA ）
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| − | **MASC（MAximized Stride with Carrier）
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| − | *本组论文
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| − | # Wenxun Zheng et al., CODIS: A New Compression Scheme for Bitmap Indexes, ANCS 2017.
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| − | # Chenxing Li et al., BAH: A Bitmap Index Compression Algorithm for Fast Data Retrieval, LCN 2016.
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| − | # Yinjun Wu et al., CAMP: A new bitmap index for data retrieval in traffic archival, Communication Letters, Vol. 20, No. 6, pp.1128 - 1131, June 2016.
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| − | # Jiahui Chang et al., SPLWAH: A bitmap index compression scheme for searching in archival Internet traffic. ICC 2015.
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| − | *研究方向
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| − | #*MPSoC增强的索引运算
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| − | ++Sebastian Haas et al., An MPSoC for Energy-Efficient Database Query Processing, DAC 2016.
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| − | ++Sebastian Haas et al., HW/SW-Database-CoDesign for Compressed Bitmap Index Processing, ASAP 2016.
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| − | #*非循环图的可达性查询
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| − | ++Sebastiaan J. van Schaik et al., A Memory Efficient Reachability Data Structure Through Bit Vector Compression, SOGMOD 2011.
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| − | #*非循环图的可达性查询
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| − | ++Gangyi Zhu et al., SciCSM: Novel Contrast Set Mining over Scientific Datasets Using Bitmap Indices, SSDBM 2015.
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| − | #* ISA增强的集运算
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| − | ++O. Arnold et al., An application-specific instruction set for accelerating set-oriented database primitives. SIGMOD 2014.
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| − | #* 有限状态机运行并行加速
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| − | ++Peng Jiang et al., Combining SIMD and Many Multi-core Parallelism for Finite State Machines with Enumerative Speculation,PPoPP 2017.
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| − | （Using Intel Xeon Phi）
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| − | = 索引（搜索引擎） =
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| − | 搜索引擎（Search Engine）是一种典型的大数据系统，是实现信息检索（Information Retrieval）的软件。
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| − | 经典的搜索引擎主要是针对爬取的网页文本的检索。 一个网页文本或文档，是由许多的单词组成的。其中每个单词可以在同一个文档中重复出现很多次，同一个单词也可以出现在不同的文档中。
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| − | ==反向索引（Inverted Index）的原理==
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| − | 为了实现快速的搜索响应，搜索引擎采用反向索引（Inverted Index）的数据结构。反向索引在信息检索中发挥重要作用。这里介绍一下前向索引（forward index）和反向索引（Inverted Index）的概念。
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| − | 前向索引(forward index)：从文档角度看其中的单词，表示每个文档（用文档ID标识）都含有哪些单词，以及每个单词出现了多少次（词频）及其出现位置（相对于文档首部的偏移量）。
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| − | 反向索引(inverted index 或inverted files)：从单词角度看文档，标识每个单词分别在那些文档中出现(文档ID)，以及在各自的文档中每个单词分别出现了多少次（词频）及其出现位置（相对于该文档首部的偏移量）。
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| − | <big>直观表示</big>：
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| − | 前向索引（一对多映射）：文档 ---> 单词
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| − | 反向索引（一对多映射）：单词 ---> 文档
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| − | 前向索引，又称为正排索引；反向索引，又称为倒排索引。但是正排和倒排索引的称谓，会造成误解。
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| − | ==反向索引（Inverted Index）的实现==
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| − | 反向索引的功能是将关键字（keyword）映射到文档（document）。
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| − | 在反向索引中，每个关键词对应一个反向链表（Inverted List），记录了该关键词出现的所有文档的编号。
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| − | 反向索引在实际实现中，可以采用位图（Bitmap）与整数数组/链表（Integer Array/List）两种结构形式。
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| − | **研究论文：
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| − | # Giuseppe Ottaviano, Nicola Tonellotto, Rossano Venturini, Optimal Space-Time Tradeoffs for Inverted Indexes, ACM WSDM 2015.
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| − | ==反向索引（Inverted Index）的运算==
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| − | 反向索引上的最重要的运算是集合交（Conjunction），并（Disjunction）和非（Negation）。
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| − | 反向索引上的交，并和非运算，对应的整数链表的实现，其操作是Intersection/Unions操作，对应位图的实现，其操作运算则是比特AND，OR，NOT操作。
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| − | **研究论文：
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| − | # Culpepper, J. Shane, and Alistair Moffat. "Efficient set intersection for inverted indexing." ACM Transactions on Information Systems (TOIS), 2010.
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| − | ==反向索引的参考实现==
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| − | [https://lucene.apache.org/core/ Lucene]
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| − | [https://pypi.python.org/pypi/Whoosh Whoosh]
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| − | [http://www.opensearchserver.com OpenSearchServer]
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