`
`Veritas Techs. LLc
`Exhibit 1026
`Page 129
`
`Veritas Techs. LLC
`Exhibit 1026
`Page 129
`
`

`

`
`
`ttachment lh: Copy
`f Hsu from the
`
`
`Illinois Institute of
`
`
`
`Technology Library
`
`
`ACTICE 8: EXPERIENCE
`
`No.10
`
`OCTOBERTQQS
`
`EDITORS
`
`DOUGLAS COMER
`
`ANDY WELLINGS
`
`WILEY
`Main-s Sun: "In?
`
`Practice &
`
`Software:
`experience
`Received on: 11-03—95
`Illinois Institute of
`Technology Library
`
`
`
`
`
`
`
`
`
`
`
`
`
`Chick-nor- Now York ' Mm - Toronto - Singnpon
`AVWkVJMMHdMMnFMNkMkm
`SPEXBL 25110) 1065-1132 £1995)
`ISSN 00330644
`
`
`
`
`
`Veritas Techs. LLc
`Exhibit 1026
`Page 130
`
`Veritas Techs. LLC
`Exhibit 1026
`Page 130
`
`

`

`Advisory Editorial Board
`Professor 0. W. BARRON
`
`Department of Electronics and Computer Science.
`University of Southampton.
`Southamptdn 509 5NH. UK.
`Professor P. J. BROWN
`Computing Laboratory. The University.
`Canterbury. Kent CT2 TNF. U.K.
`Professm J. A. CAMPBELL
`Department of Computer Science. University College London.
`Gower Street. London WC1E SBT. U.K.
`Professor F. J. CORBATO
`Electrical Engineering Department,
`Massachusetts Institute of Technology.
`545 Technolo y Square.
`Cambridge.
`assachusetts 02139. USA.
`Dr. Christopher W. FRASER
`AT&T Bell Laboratories. 500 Mountain Ave 2C-A64,
`Murray Hill. NJ 07974‘0636. USA.
`Professor PER BHINCH HANSEN
`School of Computer and Information Science.
`4-116 CST. Syracuse Universi
`.
`Syracuse. New Yorlr 13210. U.
`.A.
`Professor D. R. HANSON
`Department of Computer Science.
`Princeton University. Princeton,
`New Jersey 08544. USA.
`Professor J. KA'I'ZENELSON
`Faculty of Electrical Engineering.
`Technton-lsrael Institute of Technology.
`Haifa. Israel
`Dr. B. W. KERNIGHAN
`AT&T Bell Laboratories. 600 Mountain Avenue,
`Murray Hill. New Jersey 07974. USA.
`
`Professor D. E. KNUTH
`Department of Computer Science. Stanford University.
`Stanford. California 94305. USA.
`
`Dr. B. W. LAMPSON
`130 Lake View Ave.
`Cambridge.
`MA 02138. USA
`
`Dr. C. A. LANG
`Three-Space Ltd,
`10 Castle Street.
`Cambridge CB3 OAJ. U.l<.
`Professor B. RANDELL
`Computing Laboratory.
`University of Newcastle-upon-Tyne.
`Claremont Tower. Claremont Road.
`Newcastle-upon—Tyne NE1 TRU. U.K.
`
`Professor J. S. FlOHL
`Department of Computer Science.
`The University of Western Australia,
`Nedlands. Western Australia 6009.
`
`D. T. ROSS
`Softech Inc. 660 Totten Pond Road.
`Waltham. Massachusetts 02154. U.S.A.
`
`B. H. SHEARING
`The Software Factory,
`28 Padbrook. Limpsfield. Oxted.
`Surrey ans ODW, U.K.
`
`Professor N. WIRTH
`Institut fur Computersysterne. ETHsZentrum.
`CH73092 Ziirich. Switzerland.
`
`ttachment 1h: Copy
`of Hsu from the
`
`Illinois Institute
`
`Technolo- Librar
`
`J
`
`PRACTICE & EXPERIENCE
`Editors
`
`Professor D. E. Comer. Computer Science Department, Purdue University. West
`Lafayette, IN 47907. USA.
`Undone I. Tubfs. U.S. Editorial Assistant, Computer Science Department. Purdue University. West Lafayette.
`IN 47907. U.S.A.
`
`Dr A. J. Wellings, Department of Computer Science, University of York.
`Heslington, York YOi 5DD
`
`Avenue, Elmont. N.Y. 11003. U.S.A.
`
`Aims and Scope
`Software—Practice and Experience is an internationally respected and rigorously refereed vehicle for the dissemination and
`discussion of practical experience with new and establishd software for both systems and applications. Contributions regu-
`larly: la) describe detailed accounts of completed software-system projects which can serve as ‘hovv-to-do-it' models for future
`work in the same field: to) present short reports on programming techniques that can be used in a wide variety of areas: lci
`document new techniques and tools that aid in solving software construction problems; and id] explain methodsltechniques
`that cope with the special demands of targe scale software projects. The journal also fatures timely Short Communication
`on rapidly developing new topics.
`The editors actively encourage papers which result from practical experience with tools and methods developed and used
`in both academic and industrial environmnts. The aim is to encourage practitioners to share their experiences with design.
`implementation and evaluation of techniques and tools for software and software systems.
`Papers cover software design and implementation. case studies describing the evolution of system and the thinking behind
`them. and critical appraisals of software systems. The journal has always welcomed tutorial articles describing well-tried tech—
`niques not previously documented in computing literature. The emphasis is on practical experience; articles with theoretical
`or mathematical content are included only in cases where an understanding of the theory will lead to better practical systems.
`Articles range in length from a Short Communication lhaif to two pages) to the length required to give full treatment to a
`substantial piece of software 140 or more pages}.
`Advertising: For details contact—
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`Software—Practice and Experience llSSN 0038-06441USPS 390-9201 is published monthly. by John Wiley 5: Sons Limited, Baffins tans. Cthheslel.
`Sussex. England. Second class postage paid at Jamaica, NV. 11431. Air freight and mailing in the USA. by Publications Expedrting Servrces Inc.
`200 Meacltam Avenue. Elmonl. N.V “003 e 1595 by John Wiley at Sons Ltd. Printed and bound in Great Britain by Page Bros. Norwich. Printed
`on acid-free paper.
`ToM: Orders should be addressed to Subscriptions Department. John Wiley It Sorts Limited. Baffin: Lane. Chichester. Sussex. P019 1UD.
`England. 1995 subscription price [13 issues): u.s. $825.00.
`USA. POST‘MASTEH: Send address changes to Software—Practice and Experience. cio Publications Expediting Services Inc. 200 Meacharn
`
`Veritas Techs. LLC
`Exhibit 1026
`Page 131
`
`

`

`Attachment 1h: Copy
`of Hsu from the
`
`Illinois Institute of
`
`TeCnO _Libar
`(Softw. pract. exp.)
`
`ND EXPERIENCE
`
`VOLUME 25. ISSUE N0. 10
`
`October 1995
`
`CONTENTS
`
`Migration in Object-oriented Database Systems—A Practical Approach:
`C. Huemer, G. Kappel and S. Vieweg ....................................................... .. 1065
`
`Automatic Synthesis of Compression Techniques for Heterogeneous
`Files: W. H. Hsu and A. E. Zwarico ........................................................... .. 1097
`
`Research Alert IISII and SCISEAHCH Database (ISII.
`
`Indexed or abstracted by Cambridge Scientific Abstracts, CompuMath Citation Index liSIJ.
`Compuscience Database. Computer Contents, Computer Literature Index. Computing
`Reviews, Current Contents/Eng, Tech & Applied Sciences, Data Processing Digest. Deadline
`Newsletter, Educational Technology Abstracts. Engineering Index. Engineering Societies
`Library, IBZ (International Bibliography of Periodical Literature), Information Science Abstracts
`(Plenum), INSPEC, Knowledge Engineering Review. Nat Centre for Software Technology,
`
`A Tool for Visualizing the Execution of Interactions on a Loosely-coupled
`Distributed System: P. Ashton and J. Penny ........................................... .. 1117
`
`Process Scheduling and UNIX Semaphores: N. Dunstan and |. Fris ........ .. 1141
`
`Software Maintenance: An Approach to Impact Analysis of Objects
`Change: 8. Ajiia .......................................................................................... .. 1155
`
`SPEXBL 25110) 1065—1182 (1995)
`lSSN 0038-0644
`
`Veritas Techs. LLC
`Exhibit 1026
`Page 132
`
`

`

`Attachment 1h: Copy
`of Hsu from the
`
`Illinois Institute of
`
`TeClo Lrar .
`
`, VOL. 25(10), 1097—1116 (OCTOBER 1995)
`
`Automatic Synthesis of Compression Techniques for
`
`Heterogeneous Files
`
`WILLIAM H. HSU
`
`Revised 5 February 1995
`
`The primary motivation in studying compression is the savings in space that it provides.
`Many compression algorithms have been implemented, and with the advent of new hard-
`ware standards, more techniques are under development. Historically, research in data com-
`pression has been devoted to the development of algorithms that exploit various types of
`redundancy found in a file. The shortcoming of such algorithms is that they assume, often
`inaccurately, that files are homogeneous throughout. Consequently, each exploits only a
`subset of the redundancy found in the file.
`Unfortunately, no algorithm is effective in compressing all files.1 For example, dynamic
`Huffman coding works best on data files with a high variance in the frequency of individ-
`ual characters (including some graphics and audio data), achieves mediocre performance on
`natural language text files, and performs poorly in general on high-redundancy binary data.
`On the other hand, run length encoding works well on high-redundancy binary data, but
`performs very poorly on text files. Textual substitution works best when multiple-character
`strings tend to be repeated, as in English text, but this performance degrades as the average
`
`Department of Computer Science, University of Illinois at Urbano—Champaign, Urbana, IL 61801, U.S.A.
`(email: bhsu@cs.uiuc.edu, voice: (217) 244-1620)
`
`AND
`
`AMY E. ZWARJCO
`
`Department of Computer Science, The Johns Hopkins University, Baltimore, MD 21218, U.S.A.
`(email: amy@c5.jhu.edu, voice: (410) 51643304)
`
`SUMMARY
`
`We present a compression technique for heterogeneous files, those files which contain multiple types of
`data such as text, images, binary, audio, or animation. The system uses statistical methods to determine
`the best algorithm to use in compressing each block of data in a file (possibly a different algorithm for
`each block). The file is then compressed by applying the appropriate algorithm to each block. We obtain
`better savings than possible by using a single algorithm for compressing the file. The implementation
`of a working version of this heterogeneous compressor is described, along with examples of its value
`toward improving compression both in theoretical and applied contexts. We compare our results with
`those obtained using four commercially available compression programs, PKZIP, Unix compress, Stufilt,
`and Compact Pro, and show that our system provides better space savings.
`
`KEY WORDS:
`
`adaptive/selective data compression algorithms; redundancy metrics; heterogeneous files; program synthesis
`
`INTRODUCTION
`
`CCC 0038—0644/95/101097—20
`©1995 by John Wiley & Sons, Ltd.
`
`Received 20 April 1994
`
`Veritas Techs. LLC
`Exhibit 1026
`Page 133
`
`

`

`
`ttachment 1h: Copy
`Of Hsu from the
`
`Illinois Institute of
`
`
`
`Technology Library
`w. H. HSU AND A. E. ZWARICO
`1098
`length of these strings decreaSes. These relative strengths and weaknesses become critic
`when attempting to compress heterogeneous files. Heterogeneous files are those which co
`tain multiple types of data such as text. images, binary, audio, or animation. Consequentl
`their constituent parts may have different degrees of compressibility. Because most co
`pression algorithms are either tailored to a few specific classes of data or are designed t
`handle a single type of data at a time, they are not suited to the compression of heterog
`neous files. In attempting to apply a single method to such files, they forfeit the possibili
`of greater savings achievable by compressing various segments of the file with differe
`methods.
`To overcome this inherent weakness found in compression algorithms, we have develope
`a heterogeneous compressor that automatically chooses the best compression algorithm
`use on a given variable-length block of a file, based on both the qualitative and quanti
`tive properties of that segment. The compressor determines and then applies the select
`algorithms to the blocks separately. Assembling compression procedures to create a speci
`ically tailored program for each file gives improved performance over using one progra
`for all files. This system produces better compression results than four commonly availab
`compression packages, PKZIP,2 Unix compress,3 Stufih,‘ and Compact Pro5 for arbi
`heterogeneous files.
`The major contributions of this work are twofold. The first is an improved compressi
`system for heterogeneous files. The second is the development of a method of statis
`cal analysis of the compressibility of a file (its redundancy types). Although the conce
`of redundancy types is not new,“ synthesis of compression techniques using redundan
`measurements is largely unprecedented. The approach presented in this paper uses a straig
`forward program synthesis technique: a compression plan, consisting of instructions for ea
`block of input data, is generated, guided by the statistical properties of the input data. B
`cause of its uSe of algorithms specifically suited to the types of redundancy exhibited
`the particular input file, the system achieves consistent average performance throughout 1
`file, as shown by experimental evidence.
`As an example of the type of savings our system produces, consider compressing
`heterogeneous file (such as a small multimedia data file) consisting of 10K of low redu
`dancy (non-natural language) ASCII data, 10K of English text, and 25K of graphics.
`this casc, a reasonably sophisticated compression program might recognize the increas
`savings achievable by employing Huffman compressron, to better take advantage of the f
`that the majority of the data is graphical. However, none of the general-purpose comp
`sion methods under consideration are optimal when used alone on this file. This is becau
`the text part of this file is best compressed by textual substitution methods (e.g., Lem
`Ziv) rather than statistical methods, while the low-redundancy data“ and graphics p
`are best compressed by alphabetic distribution—based methods (e.g., arithmetic or dyn
`Huffman coding) rather than Lempel—Ziv or run-length encoding. This particular file to
`45K in length before compression. A compressor using pure dynamic Huffman coding o
`achieves about 7 per cent savings for a compressed file of length 42.2K. One of the
`general-purpose LempehZiv compressors currently available“9 achieves 18 per cent 3
`ings, producing a compressed file of length 37.4K. Our system uses arithmetic coding
`the first and last segments and Lempel—Ziv compression on the text segment in the mid
`achieving a 22 per cent savings and producing a compressed file of length 35.6K. This
`a 4 per cent improvement over the best commercial system.
`
`‘ This denotes, in our system. a file with a low rate of repeated strings.
`
`
`
`4—;
`Veritas Techs. LLc
`Exhibit 1026
`Page 134
`
`Veritas Techs. LLC
`Exhibit 1026
`Page 134
`
`

`

`ttachment 1h: Copy
`of Hsu from the
`
`Illinois Institute of
`
`Technology Library
`AUTOMATIC SYNTHESiS 0F COMPRESSION TECHNIQUES FOR HETEROGENEOUS FILES 1099
`
`The purpose of our experiments was to verify the conjecture that a selective system
`for combining methods can improve savings on a significant range of heterogeneous files,
`especially multimedia data. This combination differs from current adaptive methods in
`that it switches among compression paradigms designed to remove very different types
`of redundancy. By contrast, existing adaptive compression programs are sensitive only to
`changes in particular types of redundancy, such as run-length, which do not require changing
`the underlying algorithm used in compression. Thus they cannot adapt to changes in the
`type of redundancy present, such as from high run-length to high character repetition. The
`superiority of our approach is demonstrated in Our experimental section.
`This paper begins with a presentation of existing approaches to data compression, includ-
`ing a discussion of hybrid and adaptive compression algorithms and a description of four
`popular commercial compression packages. These are followed by documentation on the
`design of the heterogeneous compression system, analysis of experimental results obtained
`from test runs of the completed system, and comparison of the system’s performance against
`that of commercial systems. Finally, implications of the results and possibilities for future
`work are presented.
`
`a preset threshold. Another adaptive variant of this algorithm is the Lempel—Ziv—Welch
`
`RELATED WORK
`
`It is a fundamental result of information theory that there is no single algorithm that per-
`forms optimally in compressing all files.1 However, much work has been done to develop
`algorithms and techniques that work nearly optimally on all classes of files. In this sec-
`tion we discuss adaptive algorithms, composite algorithms, and four popular commercial
`compression packages.
`
`Adaptive compression algorithms and composite techniques
`
`Exploiting the heterogeneity in a file has been addreSSed in two ways: the development
`of adaptive compression algorithms, and the composition of various algorithms. Adaptive
`compression algorithms attune themselves gradually to changes in the redundancies within a
`file by modifying parameters used by the algorithm, such as the dictionary, during execution.
`For example, adaptive alphabetic distribution-based algorithms such as dynamic Huffman
`coding") maintain a tree structure to minimize the encoded length of the most frequently
`occurring characters. This property can be made to change continuously as a file is pro-
`cessed.
`
`An example of an adaptive textual substitution algorithm is Lempel—Ziv compression,
`a title which refers to two distinct variants of a basic textual substitution scheme. The
`
`first variant, known as L277 or the sliding dictionary or sliding window method, selects
`positional references from a constant range of preceding strings." ” These ‘back-pointers’
`literally encode position and length of a repeated string. The second variant, known as
`L278 or the dynamic dictionary method, uses a dictionary structure with a paging heuristic.
`When the dictionary ~ a table of strings from the file — is completely filled, the contents
`are not discarded. Instead, an auxiliary dictionary is created and updated while compression
`continues using the main dictionary. Each time this auxiliary table is filled, its contents are
`‘swapped' into the main dictionary and it is cleared. The maintenance of dictionaries for
`textual substitution is analogous to the semi-space method of garbage collection, in which
`two pages are used but only one is ‘active’ — these are exchanged when one fills beyond
`
`Veritas Techs. LLC
`Exhibit 1026
`Page 135
`
`

`

`Attachment 1h: Copy
`of Hsu from the
`
`Illinois Institute of
`
`Technology Library
`w. H. HSU AND A. E. ZWARICO
`1100
`(LZW) algorithm, a descendant of L278 used in Unix compress.“ '2 Both LZW and L278
`vary the length of strings used in compressionfi ‘3
`Yet another adaptive (alphabetic distribution-based) compression scheme, the Move—To-
`Front (MTF) method, was developed by Bentley et at.” In MTF, the ‘word code‘ for a
`symbol is determined by the position of the word in a sequential list. The word list is ordered
`so that frequently acce55ed words are near the front, thus shortening their encodings.
`Adaptive compression algorithms are not appropriate to use with heterogeneous files
`because they are Sensitive only to changes in the particular redundancy type with which
`they are associated, such as a change in the alphabetic distribution. They do not exploit
`changes across different redundancy types in the files. Therefore a so-called adaptive method
`typically cannot directly handle drastic changes in file properties, such as an abrupt transition
`from text to graphics. For example. adaptive Huffman compressors specially optimized for
`text achieve disproportionately poor performance on certain image files, and vice versa. As
`the use of multimedia files increases, files exhibiting this sort of transition will become
`more prevalent.
`Our approach differs from adaptive compression because the system chooses each algo—
`rithm (as Well as the duration of its applicability) before compression begins, rather than
`modifying the technique for each file during compression. In addition, while adaptive meth-
`ods make modifications to their compression parameters on the basis of single bytes or fixed
`length strings of inpu ,
`sor bases its compression upon statistics
`gathered from larger blocks of five kilobytes. This allows us to handle much larger changes
`in file redundancy types. This makes our system less sensitive to residual statistical fluctu-
`ations from different parts of a file. We note that it is possible to use an adaptive algorithm
`as a primitive in the system.
`Another approach to handling heterogeneous files is the composition of compression
`algorithms. Composition can either be accomplished by running several algorithms in suc—
`cession or by combining the basic algorithms and heuristics to create a new technique. For
`example, recent implementations of ‘universal’ compression programs execute the Lempel—
`Ziv algorithm and dynamic Huffman coding in succession, thus improving performance
`by combining the string repetition-based compression of Lempel—Ziv with the frequency-
`based compression strategy of dynamic Huffman coding. One commercial implementation
`is LHarc.”"5 Our system exploits the same savings since it uses the Freeze implementa—
`tion of the Lempel-Ziv algorithm, which filters Lempel—Ziv compressed output through a
`Huffman coder. An example of a truly composite technique is the compression achieved
`by using Shannon—Fano tries" in conjunction with the Fiala—Greene algorithm (a variant
`of Lempel—Ziv)” in the PKZIP2 commercial package. Tries are used to optimally encode
`strings by character frequency.” PKZIP was selected as the represcntative test program from
`this group in our experiment due to its superior performance on industrial benchmarks"
`Our approach generalizes the ideas of successively executing or combining different
`compression algorithms by allowing any combination of basic algorithms within a file. This
`includes switching from among algorithms an arbitrary number of times within a file. The
`algorithms themselves may be simple or composite and may be adaptive. All are treated as
`atomic commands to be applied to portions of a file.
`' A Me is a tree of variable degree 3 2 such that (I) each edge is labelled with a character. and the depth of any node
`represents one more than the number of characters required to identify it; (2) all internal nodes are intermediate and represent
`prefixes of keys in the irie; (3) keys (strings) may be insefled as leaves using the minimum number of characters which
`distinguish them uniquely. Thus a generic lrie containing the strings computer and compare would have keys at a depth of
`
`five which share a common prefix of length four.
`
`Veritas Techs. LLC
`Exhibit 1026
`Page 136
`
`

`

`Technolo Librar
`AUTOMATIC SYNTHESIS OF COMPRESSION TECHNIQUES FOR HETEROGENEOUS FILES 1 101
`
`The problem of heterogeneous files was addressed by Teal“ in a proposal for a naive
`heterogeneous compression system similar to ours. In such a system, files would be seg-
`mented into fixed-length encapsulated blocks; the optimal algorithm would be selected for
`each block on the basis of their simple taxonomy (qualitative data type) only; and the blocks
`would be independently compressed. Our system, however, performs more in-depth statis-
`tical analysis in order to make a more informed selection from the database of algorithms.
`This entails not only the determination of qualitative data properties but the computation of
`metrics for an entire block (as opposed to sporadic or random sampling from parts of each
`block). Furthermore, normalization constants for selection parameters (Le. the redundancy
`metrics) are fitted to observed parameters for a test library. Finally, a straightfonvard but
`crucial improvement to the naive encapsulated-block method is the implementation of a
`multi—pass scheme. By determining the complete taxonomy (data type and dominant redun—
`dancy type) in advance, any number of contiguous blocks which use the same compression
`method will be treated as a single segment. Toal observed in preliminary experiments that
`the overhead of changing compression schemes from one block to another dominated the
`additional savings that resulted from selection of a superior compression method.'8 This
`overhead is attributable to the fact that blocks compressed independently (even if the same
`method is used) are essentially separate files and assume the same startup overhead of the
`compression algorithm used.* We have determined experimentally that merging contiguous
`blocks whenever possible obviates the large majority of changes in compression method.
`This eliminates a sufficient proportion of the overhead to make heterogeneous compression
`worthwhile.
`
`ttachment lh: Copy
`of Hsu from the
`
`Illinois Institute of
`
`upwards from 4K).
`
`Commercial products
`One of the goals of this research was to develop a compression system which is gener-
`ally superior to commercially available systems. The four systems we studied are PKZIP,
`developed for microcomputers running MS-DOS;2 Unix corrlpress‘.3 and Stufflt Classic?“
`and Compact Pro,5 developed for the Apple Macintosh operating system. Each of these
`products performs its compression in a single pass, with only one method selected per file.
`Thus, the possibility of heterogeneous files is ignored.
`Unix compress uses an adaptive version of the Lempel-Ziv algorithm.6 It operates by
`substituting a fixed—length code for common substrings. compress,
`like other adaptive
`textual substitution algorithms, periodically tests its own performance and reinitializes its
`string table if the amount of compression has decreased.
`Smfi‘lt makes use of two sets of algorithms:
`it first detects special-type files such as
`image files and processes them with algorithms suited for high-resolution color data; for the
`remaining files, it queries the operating system for the explicit file type given when the file
`was created, and uses this information to choose either the LZW variant of Lempel—Ziv,4' ‘5
`dynamic Huffman coding, or run—length encoding. This is a much more limited selection
`process than what we have implemented. Additionally, no selection of compression methods
`is attempted within a file. Compact Pro uses the same methodology as Stufilt and compress,
`but incorporates an improved Lempel—Ziv derived directly from LZ'i'7.‘1 The public—domain
`version of Stufl‘lt is derived from Unix compress, as is evident from the similarity of their
`performance results.
`‘ For purposes of comparison. the block sizes tested by Toni were nearly identical to those used in our system (ranging
`
`Veritas Techs. LLC
`Exhibit 1026
`Page 137
`
`

`

`
`Attachment 1h: Copy
`of Hsu from the
`
`Illinois Institute of
`
`Technolo-
`
`Librar
`
`H
`in
`
`w. H. HSU AND A. E. ZWARICO
`1102
`Compression systems such as Stufilt perform simple selection among alternative com-
`pression algorithms. The important problem is that they are underequipped for the task of
`fitting a specific technique to each file (even when the uncompressed data is homogeneous).
`Stuflh uses few heuristics, since its algorithms are intended to be 'multipurpose'
`. Further—
`more, only the file type is considered in selecting the algorithm - that is, no measures of
`redundancy are computed. Earlier versions of Stuff]: (which were extremely similar to Unix
`compress) used composite alphabetic and textual compression, but made no selections on
`the basis of data characteristics. The chief improvements of our heterogeneous compressor
`over this approach are that it uses a two-dimensional lookup table, indexed by file proper-
`ties and quantitative redundancy metrics, and -— more important — that it treats the file as a
`collection of heterogeneous data sets.
`
`THE HETEROGENEOUS COMPRESSOR
`Our heterogeneous compressor treats a file as a collection of fixed size blocks (5K in
`the current implementation), each containing a potentially different type of data and thus
`best compressed using different algorithms. The actual compression is accomplished in
`two phases. In the first phase, the system determines the type and compressibility of each
`block. The compressibility of each block of data is determined by the values of three
`quantitative metrics representing the alphabetic distribution, the average run length and the
`string repetition ratio in the file. If these metrics are all below a certain threshold, then the
`block is considered fully compressed (uncompressible) and the program continues on to the
`next block. Otherwisc, using the block type and largest metric, the appropriate compression
`algorithm (and possible heuristic) are chosen from the compression algorithm database. The
`compression method for the current block is then recorded in a small array—based map of
`the file, and the system continues.
`The second phase comprises the actual compression and an optimization that maximizes
`the size of a segment of data to be compressed using a particular algorithm. In this optimiza-
`tion, which is interleaved with the actual compression, adjacent blocks for which exactly
`the same method have been chosen are merged into a single block. This merge technique
`maximizes the length of segments requiring a single compression method by greedin scan-
`ning ahead until a change of method is detected. Scanning is performed using the array
`map of the file generated when compression methods were selected from the database. A
`compression history. needed for decompression, is automatically generated as part of this
`phase.The newly compressed segments are written to a buffer by the algorithm, which stores
`the output data with the compression history. The system then writes out the compressed
`file and exits with a signal to the operating system that compression was successful.
`From this two-pass scheme it is straightforward to see why this system is less susceptible
`than traditional adaptive systems to biases accrued when the data type changes abruptly
`during compression. Adaptive compressors perform all operations myopically, sacrificing
`the ability to see ahead in the file or data stream to detect future fluctuations in the type
`of data. As a result, adaptive compressors retain the statistical vestiges of the old method
`until these are 'fiushed out’ by new data (or balanced out, depending upon the process for
`paging and aging internal data structures such as dictionaries). Thus adaptive compressors
`may continue to suffer the effects of bias, achieving suboptimal compression. 0n the other
`hand, by abruptly changing compression algorithms, our technique completely discards all
`remnants of the ‘previous’ method (i.e. the algorithm used on the preceding segment). This
`
`Veritas Techs. LLc
`Exhibit 1026
`Page 138
`
`Veritas Techs. LLC
`Exhibit 1026
`Page 138
`
`

`

`‘ttachment 1h: Copy
`of Hsu from the
`
`Illinois Institute of
`
`Technology Library
`
`AUTOMATIC SYNTHESIS OF COMPRESSION TECHNIQUES FOR HETEROGENEOUS FILES “03
`
`allows us to immediately capitalize on changes in data. In addition, merging contiguous
`blocks of the same data type acquires the advantage of incurring all the overhead at once
`for switching to what will be the best compression method for an entire variable-length
`segment. The primary advantage of adaptive compression techniques over our technique is
`that the adaptive compression algorithms are ‘online’ (single-pass). This property increases
`compression speed and, more important, gives the ability to compress a data stream (for
`instance, incoming data packets in a network or modem transmission) in addition to files
`in secondary storage or variable-length buffers.
`The remainder of this section presents the system. We begin with a description of the
`calculation of the block types and the redundancy metrics. We also explain the use of the
`metrics as absolute indicators of compressibility, and then describe the compression algo-
`rithms used and the structure of the database of algorithms. A discussion of implementation
`details concludes the section.
`
`compressed data, but may also include data which is merely difficult to compress. Audio
`
`The block type describes the nature of a segment of input data. There are ten classifica-
`tions of data in this system: ANSI text, non-natural language text (hexadecimal encodings of
`binary data), natural language text, computer source code, low redundancy binary, digitized
`audio, low resolution graphics, high-resolution graphics. high—redundancy binary executable,
`and binary object data. ANSI text is composed of characters from a superset of the ASCII
`alphabet. Non-natural language text contains primarily ASCII text but does not follow a
`distribution of characters like that of human languages. Examples are computer typesetting
`data, uuencoded and BinHex encoded data (which has the same character distributiou as
`binary data but is converted to text for ease of transmission). Natural language text in-
`cludes text written in English as well as other languages which are representable by the
`Roman (ASCII) alphabet. Most European languages (including the ones using th