From Dirt to Shovels: Automatic Tool Generation for Ad Hoc Data David Walker Princeton University with David Burke, Kathleen Fisher, Peter White & Kenny Q. Zhu

who am I? why am I here?

Our Common Communication Infrastructure Much information is represented in standardized data formats:   Web pages in HTML   Pictures in JPEG   Movies in MPEG   “Universal” information format XML   Standard relational database formats A plethora of data processing tools:   Visualizers (Browsers Display JPEG, HTML,...)   Query languages allow users extract information (SQL, XQuery)   Programmers get easy access through standard libraries ► ► Java XML libraries --- JAXP   Many applications handle it natively and convert back and forth ► ► MS Word

Ad Hoc Data Massive amounts of data are stored in XML, HTML or relational databases but there’s even more data that isn’t An ad hoc data format is any nonstandard, but structured data format for which convenient parsing, querying, visualizing, transformation tools are not available. (not natural language)

Ad Hoc Data from Web Server Logs (CLF) [15/Oct/1997:18:46: ] "GET /tk/p.txt HTTP/1.0" [16/Oct/1997:14:32: ] "POST /scpt/ddorg/confirm HTTP/1.0"

Ad Hoc Data from Crashreporter.log Sat Jun 24 06:38: crashdump[2164]: Started writing crash report to: /Logs/Crash/Exit/ pro.crash.log Sun Jun 25 07:23: crashreporterd[120]: mach_msg() reply failed: (ipc/send) invalid destination port

AT&T Phone Call Provisioning Data | |1| | | | ||no_ii |EDTF_6|0|MARVINS1|UNO|10| | |1| | | | ||no_ii15222| EDTF_6|0|MARVINS1|UNO|10| |20| |17| |19| |27| |29| |IA0288| |IE0288| |ED TF_CRTE| |EDTF_OS_1| |16| |26| | |1|0|0|0|0||no_ii152271|EDTF_1|0|SC1MF1F|UNO|EDTF_CRTE| |EDTF_OS_10| | |1|0|0|0|0||no_ii152270|EDTF_1|0|marshak1|UNO|EDTF_CRTE| |EDTF_OS_10|

Ad Hoc data from DNS Packets : 9192 d8fb d r : f 6d00 esearch.att.com : 00fc 0001 c00c e ' : 036e 7331 c00c 0a68 6f73 746d ns1...hostmaste : 72c0 0c77 64e e r..wd.I : 36ee e 10c0 0c00 0f e : a00 0a05 6c69 6e75 78c0 0cc0 0c linux : 0f e c00 0a07 6d61 696c mail : 6d61 6ec0 0cc0 0c e 1000 man : 0487 cf1a 16c0 0c e a0: e73 30c0 0cc0 0c e..ns b0: c0 2e03 5f67 63c0 0c _gc...! c0: d c c X.....d...phys d0: f.research.att.co

Ad Hoc data from Date: 3/21/2005 1:00PM PACIFIC Investor's Business Daily ® Stock List Name: DAVE Stock Company Price Price Volume EPS RS Symbol Name Price Change % Change % Change Rating Rating AET Aetna Inc % 31% GE General Electric Co % -8% HD Home Depot Inc % 63% IBM Intl Business Machines % -13% INTC Intel Corp % -47% Data provided by William O'Neil + Co., Inc. © All Rights Reserved. Investor's Business Daily is a registered trademark of Investor's Business Daily, Inc. Reproduction or redistribution other than for personal use is prohibited. All prices are delayed at least 20 minutes.

Ad Hoc data from !autogenerated-by: DAG-Edit version rev 3 !saved-by: gocvs !date: Fri Mar 18 21:00:28 PST 2005 !version: $Revision: $ !type: % is_a is a !type: < part_of part of !type: ^ inverse_of inverse of !type: | disjoint_from disjoint from $Gene_Ontology ; GO: <biological_process ; GO: %behavior ; GO: ; synonym:behaviour %adult behavior ; GO: ; synonym:adult behaviour %adult feeding behavior ; GO: ; synonym:adult feeding behaviour % feeding behavior ; GO: %adult locomotory behavior ; GO: ;...

The Challenge of Ad Hoc Data Data arrives “as is.” Documentation is often out-of-date or nonexistent. Data is buggy.  Missing data, “extra” data, …  Human error, malfunctioning machines, software bugs (e.g. race conditions on log entries), …  Errors are sometimes the most interesting portion of the data. Data sources may be enormous  AT&T sources can generate up to 2GB/second There are no software libraries, manuals, or armies of consultants to help you....

Raw Data Data Entry: Create Format Description Data Analysis Data Exit: Data Transformation External Systems Description libraries Automatic inference Manual customization Visual support database queries grep support google-style search binary viewer/editor anomaly detection statistical classification format-independent algorithms plug-and-play export to XML, HTML, S, database, Excel language support for custom rewriting plug-and-play ASCII log files Binary Traces Goal: An end-to-end, real-time data analysis, transformation and programming framework

The PADS System (version 1.0) [pldi 05, popl 06, popl 07] “Ad Hoc” Data Source Analysis Report XML PADS Data Description PADS Compiler Generated Libraries (Parsing, Printing, Traversal) PADS Runtime System (I/O, Error Handling) XML Converter Data Profiler Graphing Tool Query Engine Custom App GraphInformation ? generic description- directed programs coded once written by hand

Trivial Example Data Sources: type payload = union { int32 i; stringFW(3) s2; }; type source = struct { ‘\”’; payload p1; “,”; payload p2; ‘\”’; } “0, 24” “foo, 16” “bar, end” Description: Key points to know:  Descriptions based on programming language “types”  Broad collection of “base types” (ints, strings, dates, ip addresses...)  Structured types includes “structs,” “unions” and “arrays” .... but has many other features: dependency, constraints, recursion,...  has formal semantics & proven properties

The PADS System (version 2.0) Tokenization Structure Discovery Format Refinement Data Description Scoring Function Raw Data PADS Compiler Profiler XMLifier Analysis Report XML Format Inference Structure Discovery Format Refinement

Structure Discovery: Overview Top-down, divide-and-conquer algorithm:  Compute various statistics from tokenized data  Guess a top-level type constructor  Partition tokenized data into smaller chunks  Recursively analyze and compute types from smaller chunks “0, 24” “foo, 16” “bar, end” “ INT, INT ” “ STR, INT ” “ STR, STR ” tokenize

Structure Discovery: Overview Top-down, divide-and-conquer algorithm:  Compute various statistics from tokenized data  Guess a top-level type constructor  Partition tokenized data into smaller chunks  Recursively analyze and compute types from smaller chunks “ INT, INT ” “ STR, INT ” “ STR, STR ” discover “”, ?? struct ? candidate structure so far INT STR INT STR sources

Structure Discovery: Overview Top-down, divide-and-conquer algorithm:  Compute various statistics from tokenized data  Guess a top-level type constructor  Partition tokenized data into smaller chunks  Recursively analyze and compute types from smaller chunks discover “”, ?? struct INT STR INT STR “”, ? ? struct union INT ? STR INT STR

Structure Discovery: Details Compute frequency distribution histogram for each token. (And recompute at every level of recursion). “ INT, INT ” “ STR, INT ” “ STR, STR ” percentage of sources Number of occurrences per source

Structure Discovery: Details Cluster tokens into groups with similar histograms  Similar histograms ► strong evidence tokens coexist in same description component ► use symmetric relative entropy to measure similarity  Only the “shape” of the histogram matters ► normalize histograms by sorting columns in descending size ► result: comma & quote grouped together

Structure Discovery: Details Find most promising token group to divide and conquer:  Structs == Groups with high coverage & low “residual mass”  Arrays == Groups with high coverage, sufficient width & high “residual mass”  Unions == Other token groups Struct involving comma, quote identified in histogram above Overall procedure gives good starting point for rewriting system

Format Refinement Reanalyze example data with aid of rough description Rewrite format description to:  simplify presentation ► merge & rewrite structures  improve precision ► reorganize description structure ► add constraints (sortedness, uniqueness, linear relations, functional dependencies)  fill in missing details ► find completions where structure discovery bottoms out ► refine base types (termination conditions for strings, integer sizes)

Format Refinement Three main sub-phases  Phase 1: Tagging/Table generation ► Convert rough description into tagged description + relational table  Phase 2: Constraint inference ► Analyze table and infer constraints ► Use TANE algorithm [Huhtala et al. 99]  Phase 3: Format rewriting ► Use inferred constraints & type isomorphisms to rewrite rough description ► Greedy search to optimize information-theoretic score

Refinement: Simple Example

“0, 24” “foo, beg” “bar, end” “0, 56” “baz, middle” “0, 12” “0, 33” …

“0, 24” “foo, beg” “bar, end” “0, 56” “baz, middle” “0, 12” “0, 33” … struct “ ”, union int alpha int alpha structure discovery

“0, 24” “foo, beg” “bar, end” “0, 56” “baz, middle” “0, 12” “0, 33” … struct “ ”, union int alpha int alpha structure discovery (id2) struct “ ”, union int (id3) tagging/ table gen (id1) id1id id alpha (id4) int (id5)alpha(id6) id4 -- id5... id foobeg

“0, 24” “foo, beg” “bar, end” “0, 56” “baz, middle” “0, 12” “0, 33” … struct “ ”, union int alpha int alpha structure discovery (id2) struct “ ”, union int (id3) tagging/ table gen (id1) id3 = 0 id1 = id2 (first union is “int” whenever second union is “int”) constraint inference id1id id alpha (id4) int (id5)alpha(id6) id4 -- id5... id foobeg

“0, 24” “foo, beg” “bar, end” “0, 56” “baz, middle” “0, 12” “0, 33” … struct “ ”, union int str int str structure discovery (id2) struct “ ”, union int (id3) tagging/ table gen (id1) id3 = 0 id1 = id2 (first union is “int” whenever second union is “int”) constraint inference rule-based structure rewriting struct “ ” union 0str int str struct,, id1id id more accurate: -- first int = 0 -- rules out “int, alpha-string” records str (id4) int (id5)str(id6) id4 -- id5... id foobeg

Biggest Weakness Degree of success often hinges on the inference system having a tokenization scheme that matches the tokenization scheme of the data source. Good tokens capture high-level, human abstractions compactly. Techniques for learning tokenizations from data directly? Techniques for using multiple, ambiguous tokenization schemes simultaneously?

Related Work Most common domains for grammar inference:  xml/html  natural language Systems that focus on ad hoc data rare and the few that don’t support PADS tool suite:  Rufus system ’93, TSIMMIS ’94, Potter’s Wheel ’01 Top-down structure discovery  Arasu & Garcia-Molina ’03 (extracting data from web pages) Grammar induction using MDL & grammar rewriting search  Stolcke and Omohundro ’94 “Inducing probabilistic grammars...”  T. W. Hong ’02, Ph.D. thesis on information extraction from web pages  Higuera ’01 “Current trends in grammar induction”

Conclusions Still a work in progress, but we are able to produce XML and statistical reports fully automatically from ad hoc data sources. We’ve tested on approximately 15 real, mostly systemy data sources (web logs, crash reports, AT&T phone call data, etc.) with what we believe is relatively good success For papers & software, see our website at:

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