UCLA Computer Science Department Highperformance Pattern Detection and

UCLA Computer Science Department High-performance Pattern Detection and Discovery for Databases and Data Streams Barzan Mozafari Adviser: Prof. Carlo Zaniolo Committee Members: Prof. Junghoo Cho, Prof. D. Stott Parker, and Prof. Mark Hansen Winter 2011

tte Pa ery Qu es tch Ma 1. Query Languages that allow for the expression of complex patterns 2. Scalable Systems that support such languages and can handle massive, high-arrival data 3. Efficient, One-pass Algorithms that can mine large amounts of stored or streaming data and extract useful patterns rn s Big Picture Dat Min a ing Data

Overview • Introduction • Query Languages for Pattern Detection – – Kleene-* Constructs in SQL Nested Words [SIGMOD’ 10, VLDB’ 10] Optimization [Work in progress] XSeq [Work in progress] • Conclusion

Complex Event Patterns • Sequences in DBs and CEP over data streams • Academic and industrial interest: – SQL-TS [PODS ‘ 01] – SASE [2006], SASE+ [2008] – SQL Change proposal, 2007 (by Oracle, IBM and Streambase) – Other industrial and academic languages: • Cayuga & CEL • CEDR • Microsoft CEP & LINQ

Our Contribution: K*SQL 1. A powerful language for: i. Expressing more complex patterns on relational streams and sequences ii. Querying data with more complex structures, e. g, XML and genomic data 2. A unifying engine for sequence patterns and XML 3. New optimization techniques • pattern search over nested words 4. Efficient query execution backend for other languages 5. XSeq: An XPath-resembling language to bring Kleene-* to XML applications

Regular Expressions in SQL rfid_readings (Time, Sensor. Type, ensor. Id, Item. Id)

Nested Kleene-*: K*SQL Timestamp Badge. ID Room 1226633804799 26 Room 12 1226633805799 2 Room 7 1226633806799 26 Room 14 1226633807799 5 Room 37 1226633808799 5 Room 37 … … …

Employees who spend >1 hour in the lab but leave without going to decontamination room SELECT badge. ID Lab FROM rfid PARTITION BY badge. ID ORDER BY timestamp AS PATTERN Room 2 Room 12 Room 7 Lab Room 2 Room 7 Exit

Employees who spend >1 hour in the lab but leave without going to decontamination room SELECT badge. ID FROM rfid PARTITION BY badge. ID ORDER BY timestamp AS PATTERN ( L ) WHERE L. room = ‘Lab’ L Lab Room 2 Room 12 Room 7 Lab Room 2 Room 7 Exit

Employees who spend >1 hour in the lab but leave without going to decontamination room SELECT badge. ID FROM rfid PARTITION BY badge. ID ORDER BY timestamp AS PATTERN ( L+ ) WHERE L. room = ‘Lab’ L+ L Lab Room 2 Room 12 Room 7 Lab Room 2 Room 7 Exit

Employees who spend >1 hour in the lab but leave without going to decontamination room SELECT badge. ID FROM rfid PARTITION BY badge. ID ORDER BY timestamp AS PATTERN ( L+ O+ ) WHERE L. room = ‘Lab’ AND O. room != ‘Decontamination’ L+ O+ L Lab O Room 2 O Room 12 O Room 7 Lab Room 2 Room 7 Exit

Employees who spend >1 hour in the lab but leave without going to decontamination room SELECT badge. ID L+ R FROM rfid PARTITION BY badge. ID ORDER BY timestamp AS PATTERN ( (R: L+ O+) ) WHERE L. room = ‘Lab’ AND O. room != ‘Decontamination’ O+ L+ R O+ L Lab R Room 2 R Room 12 R Room 7 L Lab R Room 2 R Room 7 Exit

Employees who spend >1 hour in the lab but leave without going to decontamination room SELECT badge. ID L+ R FROM rfid PARTITION BY badge. ID ORDER BY timestamp AS PATTERN ( (R: L+ O+)+ ) R+ WHERE L. room = ‘Lab’ AND O. room != ‘Decontamination’ O+ L+ R O+ L Lab R Room 2 R Room 12 R Room 7 L Lab R Room 2 R Room 7 Exit

Employees who spend >1 hour in the lab but leave without going to decontamination room SELECT badge. ID L+ R FROM rfid PARTITION BY badge. ID ORDER BY timestamp AS PATTERN ( (R: L+ O+)+ X) R+ WHERE L. room = ‘Lab’ AND O. room != ‘Decontamination’ AND X. room = ‘Exit’ O+ L+ R O+ L Lab R Room 2 R Room 12 R Room 7 L Lab R Room 2 R Room 7 X Exit

Employees who spend >1 hour in the lab but leave without going to decontamination room SELECT badge. ID L+ R FROM rfid PARTITION BY badge. ID ORDER BY timestamp AS PATTERN ( (R: L+ O+)+ X) R+ WHERE L. room = ‘Lab’ AND O. room != ‘Decontamination’ AND X. room = ‘Exit’ AND sum(R. Last(L). timestamp – R. First(L). timestamp) > 3600 O+ L+ R O+ L Lab R Room 2 R Room 12 R Room 7 L Lab R Room 2 R Room 7 X Exit

Strictly More Expressive, through: (i)Nested Kleene-*, (ii) Labels, i. e. Aliases SELECT badge. ID FROM rfid PARTITION BY badge. ID ORDER BY timestamp AS PATTERN ( (R: L+ O*)+ X) WHERE L. room = ‘Lab’ AND O. room != ‘Decontamination’ AND X. room = ‘Exit’ AND sum(R. Last(L). timestamp – R. First(L). timestamp) > 3600

Strictly More Expressive, through: (i)Nested Kleene-*, (ii) Labels, i. e. Aliases SELECT badge. ID, L+ Last(R). Last(L). timestamp – First(R). First(L). timestamp) R FROM rfid + O PARTITION BY badge. ID ORDER BY timestamp AS PATTERN ( (R: L+ O*)+ X) L+ R+ WHERE L. room = ‘Lab’ R AND O. room != ‘Decontamination’ O+ AND X. room = ‘Exit’ AND sum(R. Last(L). timestamp – R. First(L). timestamp) > 3600 L Lab R Room 2 R Room 12 R Room 7 L Lab R Room 2 R Room 7 X Exit

K*SQL Checkpoint 1. A powerful language with a very efficient implementation based on FSA 2. Subsumes SQL-MR, SASE+, Cayuga, SQL -TS 3. Many interesting applications – including queries on semistructured documents Very natural question: Can we handle full XML?

Automata and XML Word Automata (FSA): only linear structure is explicit, cannot model parenthesis languages Ordered Tree Automata (OTA): only hierarchical structure is explicit, exponentially less succinct for word queries Pushdown Automata (PDA): Many problems are undecidable; expensive complexity

Advances in the Automata World Nested Words [Alur’ 06] Linear sequence + well-nested edges Positions labeled with symbols in S a 1 a 2 a 3 a 4 a 5 a 6 a 7 a 8 a 9 a 10 a 11 a 12 Positions classified as: Call positions: both linear and hierarchical successors Return positions: both linear and hierarchical predecessors Internal positions: otherwise 20

Nested Word Applications XML Document <conference> <name> CAV 2006 </name> <location> <city> Seattle </city> <hotel> Sheraton </hotel> </location> <sponsor> MSR </sponsor> <sponsor> Cadence </sponsor> </conference> Program global int x; bool P() { … x = 3; if Q x = 1 ; … } bool Q () { local int y; … x = y; return (x==0); } RNA Sequence Primary structure: Linear sequence of nucleotides (A, C, G, U) Secondary structure: Hydrogen bonds between nucleotides U G C A A C U G C A C G G U

Odious Comparison Property FSA NWA PDA input is read from left to right Yes Yes Deterministic automata as expressive as non-deterministic ones Yes No Closed under complementation Yes Only for DPDA w/ final state Closed under union, intersection, concatenation, and Kleene-* Yes No Emptiness Decidable membership, language inclusion, language equivalence Decidable Undecidable Can recognize paranthesis languages? No Yes NWA is exponentially more succinct than Tree Automata No query language has been proposed for NW

XML Sigmod Record: SAX-3 <!ELEMENT Sigmod. Record (issue)* > <!ELEMENT issue (volume, number, articles) > <!ELEMENT volume (#PCDATA)> <!ELEMENT number (#PCDATA)> <!ELEMENT articles (article)* > <!ELEMENT article (title, init. Page, end. Page, autho rs) > <!ELEMENT title (#PCDATA)> <!ELEMENT init. Page (#PCDATA)> <!ELEMENT end. Page tag. I nde x Type Token Value 1 open Sigmod Record _ 2 open issue _ 3 open volume _ 4 text _ 11 5 close volume _ 6 open number _ … … 25 open author _ 26 attribute positio n 01 27 text _ Karen Botnich

<Sigmod. Record> <issue> … <article> <title> Implementation of GEM </title> <init. Page> 45 </init. Page> … <authors> … <author position="01"> Carlo Zaniolo </author> … </authors> </article> …. XPath Find articles of Carlo Zaniolo as the 2 nd co-author //article [authors/author [@position = "01" and text()="Carlo Zaniolo"] ]/title/text()

K*SQL Question: Can we query nested words in K*SQL? In particular: can we express traditional XML queries – i. e. those often expressed via XPath/XQuery:

Find articles of Carlo Zaniolo as the 2 nd co-author SELECT Title. token AS article. Name FROM sigmod_record AS PATTERN ( ) WHERE <Sigmod. Record> <issue> … <article> <title> Implementation of GEM </title> <init. Page> 45 </init. Page> … <aut hors> … <author position="01"> Carlo Zaniolo </author> … </authors> </article> ….

Find articles of Carlo Zaniolo as the 2 nd co-author SELECT Title. token AS article. Name FROM sigmod_record AS PATTERN (Op. Art ) WHERE Op. Art. value = ‘<article>’ <Sigmod. Record> <issue> … <article> <title> Implementation of GEM </title> <init. Page> 45 </init. Page> … <authors> … <author position="01"> Carlo Zaniolo </author> … </authors> </article> ….

Find articles of Carlo Zaniolo as the 2 nd co-author SELECT Title. token AS article. Name FROM sigmod_record AS PATTERN (Op. Art ) WHERE Op. Art = open(‘article’) <Sigmod. Record> <issue> … <article> <title> Implementation of GEM </title> <init. Page> 45 </init. Page> … <authors> … <author position="01"> Carlo Zaniolo </author> … </authors> </article> ….

Find articles of Carlo Zaniolo as the 2 nd co-author SELECT Title. token AS article. Name FROM sigmod_record AS PATTERN (Op. Art Op. Titl ) WHERE Op. Art = open(‘article’) AND Op. Titl = open(‘title’) <Sigmod. Record> <issue> … <article> <title> Implementation of GEM </title> <init. Page> 45 </init. Page> … <authors> … <author position="01"> Carlo Zaniolo </author> … </authors> </article> ….

Find articles of Carlo Zaniolo as the 2 nd co-author SELECT Title. token AS article. Name FROM sigmod_record AS PATTERN (Op. Art Op. Title ) WHERE Op. Art = open(‘article’) AND Op. Titl = open(‘title’) <Sigmod. Record> <issue> … <article> <title> Implementation of GEM </title> <init. Page> 45 </init. Page> … <authors> … <author position="01"> Carlo Zaniolo </author> … </authors> </article> ….

Find articles of Carlo Zaniolo as the 2 nd co-author SELECT Title. token AS article. Name FROM sigmod_record AS PATTERN (Op. Art Op. Title Cl. Titl ) WHERE Op. Art = open(‘article’) AND Op. Titl = open(‘title’) AND Cl. Titl = close(‘title’) <Sigmod. Record> <issue> … <article> <title> Implementation of GEM </title> <init. Page> 45 </init. Page> … <authors> … <author position="01"> Carlo Zaniolo </author> … </authors> </article> ….

Find articles of Carlo Zaniolo as the 2 nd co-author SELECT Title. token AS article. Name FROM sigmod_record AS PATTERN (Op. Art Op. Title Cl. Titl E* ) WHERE Op. Art = open(‘article’) AND Op. Titl = open(‘title’) AND Cl. Titl = close(‘title’) AND is. Element(E) <Sigmod. Record> <issue> … <article> <title> Implementation of GEM </title> <init. Page> 45 </init. Page> … <authors> … <author position="01"> Carlo Zaniolo </author> … </authors> </article> ….

Find articles of Carlo Zaniolo as the 2 nd co-author SELECT Title. token AS article. Name FROM sigmod_record AS PATTERN (Op. Art Op. Title Cl. Titl E* Op. Auths ) WHERE Op. Art = open(‘article’) AND Op. Titl = open(‘title’) AND Cl. Titl = close(‘title’) AND is. Element(E) AND Op. Auths = open(‘authors’) <Sigmod. Record> <issue> … <article> <title> Implementation of GEM </title> <init. Page> 45 </init. Page> … <authors> … <author position="01"> Carlo Zaniolo </author> … </authors> </article> ….

Find articles of Carlo Zaniolo as the 2 nd co-author <Sigmod. Record> <issue> … SELECT Title. token AS article. Name <article> <title> FROM sigmod_record Implementation of GEM AS PATTERN </title> (Op. Art Op. Title Cl. Titl E* <init. Page> Op. Auths E* 45 ) </init. Page> WHERE Op. Art = open(‘article’) … AND Op. Titl = open(‘title’) AND Cl. Titl = close(‘title’) <authors> AND is. Element(E) AND Op. Auths = open(‘authors’) AND Cl. Art = close(‘article’) … <author position="01"> Carlo Zaniolo </author> … </authors> </article> ….

Find articles of Carlo Zaniolo as the 2 nd co-author <Sigmod. Record> <issue> … SELECT Title. token AS article. Name <article> <title> FROM sigmod_record Implementation of GEM AS PATTERN </title> (Op. Art Op. Title Cl. Titl E* <init. Page> Op. Auths E* Op. Au 45 ) </init. Page> WHERE Op. Art = open(‘article’) … AND Op. Titl = open(‘title’) AND Cl. Titl = close(‘title’) <authors> … AND is. Element(E) AND Op. Auths = open(‘authors’) AND Op. Au = open(‘author’) <author position="01"> Carlo Zaniolo </author> … </authors> </article> ….

Find articles of Carlo Zaniolo as the 2 nd co-author <Sigmod. Record> <issue> … SELECT Title. token AS article. Name <article> <title> FROM sigmod_record Implementation of GEM AS PATTERN </title> (Op. Art Op. Title Cl. Titl E* <init. Page> Op. Auths E* Op. Au Pos 45 ) </init. Page> WHERE Op. Art = open(‘article’) … AND Op. Titl = open(‘title’) AND Cl. Titl = close(‘title’) <authors> … AND is. Element(E) <author AND Op. Auths = open(‘authors’) AND Op. Au = open(‘author’) AND pos. type = ‘attr’ AND pos. value = ’ 01’ AND pos. token = ‘position’ position="01"> Carlo Zaniolo </author> … </authors> </article> ….

Find articles of Carlo Zaniolo as the 2 nd co-author <Sigmod. Record> <issue> … SELECT Title. token AS article. Name <article> <title> FROM sigmod_record Implementation of GEM AS PATTERN </title> (Op. Art Op. Title Cl. Titl E* <init. Page> Op. Auths E* Op. Au Pos 45 ) </init. Page> WHERE Op. Art = open(‘article’) … AND Op. Titl = open(‘title’) AND Cl. Titl = close(‘title’) <authors> … AND is. Element(E) <author AND Op. Auths = open(‘authors’) position="01"> AND Op. Au = open(‘author’) Carlo Zaniolo AND pos = attribute (‘position’, ’ 01’) </author> … </authors> </article> ….

Find articles of Carlo Zaniolo as the 2 nd co-author <Sigmod. Record> <issue> … SELECT Title. token AS article. Name <article> <title> FROM sigmod_record Implementation of GEM AS PATTERN </title> (Op. Art Op. Title Cl. Titl E* <init. Page> Op. Auths E* Op. Au Pos Author 45 ) </init. Page> WHERE Op. Art = open(‘article’) … AND Op. Titl = open(‘title’) AND Cl. Titl = close(‘title’) <authors> … AND is. Element(E) <author AND Op. Auths = open(‘authors’) position="01"> AND Op. Au = open(‘author’) AND pos = attribute(‘position’, ‘ 01’) AND author. token = `Carlo Zaniolo’ Carlo Zaniolo </author> … </authors> </article> ….

Find articles of Carlo Zaniolo as the 2 nd co-author <Sigmod. Record> <issue> … SELECT Title. token AS article. Name <article> <title> FROM sigmod_record Implementation of GEM AS PATTERN </title> (Op. Art Op. Title Cl. Titl E* <init. Page> Op. Auths E* Op. Au Pos Author Cl. Au 45 ) </init. Page> WHERE Op. Art = open(‘article’) … AND Op. Titl = open(‘title’) AND Cl. Titl = close(‘title’) <authors> … AND is. Element(E) <author AND Op. Auths = open(‘authors’) position="01"> AND Op. Au = open(‘author’) Carlo Zaniolo AND pos = attribute(‘position’, ‘ 01’) AND author. value = `Carlo Zaniolo’ AND Cl. Au = close(‘author’) </author> … </authors> </article> ….

Find articles of Carlo Zaniolo as the 2 nd co-author <Sigmod. Record> <issue> … SELECT Title. token AS article. Name <article> <title> FROM sigmod_record Implementation of GEM AS PATTERN </title> (Op. Art Op. Title Cl. Titl E* <init. Page> Op. Auths E* Op. Au Pos Author Cl. Au E* 45 ) </init. Page> WHERE Op. Art = open(‘article’) … AND Op. Titl = open(‘title’) AND Cl. Titl = close(‘title’) <authors> … AND is. Element(E) <author AND Op. Auths = open(‘authors’) position="01"> AND Op. Au = open(‘author’) Carlo Zaniolo AND pos = attribute(‘position’, ‘ 01’) </author> AND author. value = `Carlo Zaniolo’ AND Cl. Au = close(‘author’) … </authors> </article> ….

Find articles of Carlo Zaniolo as the 2 nd co-author <Sigmod. Record> <issue> … SELECT Title. token AS article. Name <article> <title> FROM sigmod_record Implementation of GEM AS PATTERN </title> (Op. Art Op. Title Cl. Titl E* <init. Page> Op. Auths E* Op. Au Pos Author Cl. Au E* 45 Cl. Auths Cl. Art) </init. Page> WHERE Op. Art = open(‘article’) … AND Op. Titl = open(‘title’) AND Cl. Titl = close(‘title’) <authors> … AND is. Element(E) <author AND Op. Auths = open(‘authors’) position="01"> AND Op. Au = open(‘author’) Carlo Zaniolo AND pos = attribute(‘position’, ‘ 01’) </author> AND author. token = `Carlo Zaniolo’ … AND Cl. Au = close(‘author’) AND Cl. Auths = close(‘authors’) AND Cl. Art = close(‘article’) </authors> </article> ….

Find articles of Carlo Zaniolo as the 2 nd co-author <Sigmod. Record> <issue> … SELECT Title. token AS article. Name <article> <title> FROM sigmod_record Implementation of GEM AS PATTERN </title> (Op. Art Op. Title Cl. Titl E* <init. Page> Op. Auths E* Op. Au Pos Author Cl. Au E* 45 Cl. Auths Cl. Art) </init. Page> WHERE Op. Art = open(‘article’) … AND Op. Titl = open(‘title’) AND Cl. Titl = close(‘title’) <authors> … AND is. Element(E) <author AND Op. Auths = open(‘authors’) position="01"> AND Op. Au = open(‘author’) Carlo Zaniolo AND pos = attribute(‘position’, ‘ 01’) </author> AND author. token = `Carlo Zaniolo’ … AND Cl. Au = close(‘author’) </authors> AND Cl. Auths = close(‘authors’) </article> AND Cl. Art = close(‘article’) ….

Find articles of Carlo Zaniolo as the 2 nd co-author <Sigmod. Record> <issue> … SELECT Title. token AS article. Name <article> <title> FROM sigmod_record Implementation of GEM AS PATTERN </title> (Op. Art Op. Title Cl. Titl E* <init. Page> Op. Auths E* Op. Au Pos Author Cl. Au E* 45 Cl. Auths Cl. Art) </init. Page> WHERE Op. Art = open(‘article’) … AND Op. Titl = open(‘title’) AND Cl. Titl = close(‘title’) <authors> … AND is. Element(E) <author AND Op. Auths = open(‘authors’) position="01"> AND Op. Au = open(‘author’) Carlo Zaniolo AND pos = attribute(‘position’, ‘ 01’) </author> AND author. token = `Carlo Zaniolo’ … AND Cl. Au = close(‘author’) </authors> AND Cl. Auths = close(‘authors’) </article> AND Cl. Art = close(‘article’) ….

Sequence Queries over XML: ‘W’-Patterns in Stocks <!ELEMENT Stocks (Stock)* > <!ELEMENT Stock (symbol, date, price, volume)> <!ELEMENT symbol (#PCDATA)> <!ELEMENT date (#PCDATA)> <!ELEMENT price (#PCDATA)> <!ELEMENT volume (#PCDATA)>

W-patterns in NASDAQ transactions with volume>1000 SELECT FIRST(Z). FIRST(X). Sym. token FROM Nasdaq PARTITION BY Y. X. Sym. token AS PATTERN (Z: (X: Op. St Sym Date OP Price 1 CP Op. V Volume Cl. V Cl. St)* (Y: Op. St Sym Date OP Price 2 CP Op. V Volume Cl. V Cl. St)* )^2 WHERE Op. St = open(‘Stock’) AND Cl. St = open(‘Stock’) AND OP = open(‘price’) AND CP = close(‘price’) AND Op. V = open(‘volume’) AND Cl. V = close(‘volume’) AND INT(volume. token) >= 100 AND Z. X. price 1. token < Z. PREV(X). price 1. token AND Z. Y. price 2. token > Z. PREV(Y). price 2. token <Stock symbol=“YHOO” date=“ 01 -01 -2010 23: 10: 00”> <price> 18. 50 </price> <volume> 21 </volume> </Stock> <Stock symbol=“YHOO” date=“ 01 -01 -2010 23: 16: 00”> <price> 18. 70 </price> <volume> 11 </volume> </Stock> …

W-patterns in NASDAQ transactions with volume>1000 SELECT FIRST(Z). FIRST(X). Sym. token FROM Nasdaq PARTITION BY Y. X. Sym. token AS PATTERN (Z: (X: Op. St Sym Date OP Price 1 CP Op. V Volume Cl. V Cl. St)* (Y: Op. St Sym Date OP Price 2 CP Op. V Volume Cl. V Cl. St)* )^2 WHERE Op. St = open(‘Stock’) AND Cl. St = open(‘Stock’) AND OP = open(‘price’) AND CP = close(‘price’) AND Op. V = open(‘volume’) AND Cl. V = close(‘volume’) AND INT(volume. token) >= 100 AND Z. X. price 1. token < Z. PREV(X). price 1. token AND Z. Y. price 2. token > Z. PREV(Y). price 2. token <Stock symbol=“YHOO” date=“ 01 -01 -2010 23: 10: 00”> <price> 18. 50 </price> <volume> 21 </volume> </Stock> <Stock symbol=“YHOO” date=“ 01 -01 -2010 23: 16: 00”> <price> 18. 70 </price> <volume> 11 </volume> </Stock> …

W-patterns in NASDAQ transactions with volume>1000 SELECT FIRST(Z). FIRST(X). Sym. token FROM Nasdaq PARTITION BY Y. X. Sym. token AS PATTERN (Z: (X: Op. St Sym Date OP Price 1 CP Op. V Volume Cl. V Cl. St)* (Y: Op. St Sym Date OP Price 2 CP Op. V Volume Cl. V Cl. St)* )^2 WHERE Op. St = open(‘Stock’) AND Cl. St = open(‘Stock’) AND OP = open(‘price’) AND CP = close(‘price’) AND Op. V = open(‘volume’) AND Cl. V = close(‘volume’) AND INT(volume. token) >= 100 AND Z. X. price 1. token < Z. PREV(X). price 1. token AND Z. Y. price 2. token > Z. PREV(Y). price 2. token X* Y* <Stock symbol=“YHOO” date=“ 01 -01 -2010 23: 10: 00”> <price> 18. 50 </price> <volume> 21 </volume> </Stock> <Stock symbol=“YHOO” date=“ 01 -01 -2010 23: 16: 00”> <price> 18. 70 </price> <volume> 11 </volume> </Stock> …

Optimization in K*SQL • Compile-Time: – Inferring inter-predicate implications – Query re-writing, e. g. adding more constrainst – Greedy predicate assignment • Run-Time: Avoiding unnecessary backtracks – VPSearch: Extending KMP search algorithm to nested words and visibly pushdown words – Optimizing non-determinisitc queries • i. e. all-match query modes

K*SQL vs. XML Engines

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