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1 Introduction ATLaS a Small but Complete SQL Extension for Data Mining and Data Streams

ATLaS:a Small but Complete SQL Extension for Data Mining and Data Streams 1Haixun Wang2Carlo Zaniolo2Chang Richard Luo

1IBM T.J.Watson Research Center haixun@https://www.wendangku.net/doc/4714049800.html, 2Computer Science Dept,UCLA {zaniolo,lc}@https://www.wendangku.net/doc/4714049800.html,

1Introduction

DBMSs have long suffered from SQL’s lack of power and extensibility.We have implemented ATLaS[1],a powerful database language and system that enables users to develop complete data-intensive applications in SQL—by writing new aggregates and table functions in SQL,rather than in procedural languages as in current Object-Relational sys-tems.As a result,ATLaS’SQL is Turing-complete[7],and is very suitable for advanced data-intensive applications, such as data mining and stream queries.The ATLaS system is now available for download along with a suite of applica-tions[1]including various data mining functions,that have been coded in ATLaS’SQL,and execute with a modest (20–40%)performance overhead with respect to the same applications written in C/C++.Our proposed demo will illustrate the key features and applications of ATLaS.In particular,we will demonstrate:

?ATLaS’SQL features,including its native support for user-de?ned aggregates and table functions.?Advanced database applications supported by ATLaS’SQL,including continuous queries on data streams and data mining applications such as classi?ers main-tained over concept-drifting data streams.

?The ATLaS system,including its architecture,query rewriting and optimization techniques,and the data stream management module.

2ATLaS’SQL

ATLaS adopts from SQL-3the idea of specifying user de-?ned aggregates(UDAs)by an initialize,an iterate,and a terminate computation;however,ATLaS let users express these three computations by a single procedure written in

detected.This gives the UDA of Example2,where the RETURN statements appear in ITERATE instead of TERMI-NATE.The UDA online

avg(Next Int):Real

{T ABLE state(tsum Int,cnt Int);

INITIALIZE:{

INSERT INTO state VALUES(Next,1);

}

ITERA TE:{

UPDA TE state

SET tsum=tsum+Next,cnt=cnt+1;

INSERT INTO RETURN

SELECT sum/cnt FROM state

WHERE cnt%200=0;

}

TERMINA TE:{}

}

Straightforward as it appears to be,ATLaS’approach to aggregate de?nition greatly improves the expressive power and extensibility of SQL.For instance,in ATLaS,UDAs can call other UDAs,including themselves.This enables us to compute,for example,the transitive closure of a graph using a UDA that performs a depth-?rst traversal of the graph by recursively calling itself.In fact,we proved that SQL so extended is Turing-complete on database tables[7]. As a result,we can express in ATLAS advanced applica-tions such as data mining that are dif?cult to support well using the current SQL-compliant DBMSs[6].

3Stream Applications in ATLaS

SQL extensions have been proposed to support continuous queries in SQL[2].ATLaS’SQL is Turing complete,as a result,stream queries can be implemented in ATLaS with-out additional language constructs.

ATLaS supports a delta-based computation of aggre-gates on windows(Example3).UDAs on windows are de-?ned using three states INITIALIZE,ITERATE,and REVISE (which replaces TERMINATE).The?rst two states are ac-tive in the transient situation,when the query is?rst started on the stream,and the boundary of the window have not yet been reached.Once the boundary of the window have been reached then ITERATE is no longer true,and every new incoming tuple is processed by REVISE.In this state, the system maintains the EXPIRED table holding the input tuples that just expired(one for count-based windows,zero, one,or many for time-span based windows).This table has the same schema as the input tuples,(i.e.,EXPIRED(Next Int)for Example3),and it is updated automatically by the system.Thus,the sum and the count of the tuples in EX-PIRED can now be used to update the sum and the count, and then return the average value of the window.

Example3De?ning avg on windows

AGGREGA TE myavg(Next Int):Real

{T ABLE state(tsum Int,cnt Int);

INITIALIZE:{

INSERT INTO state VALUES(Next,1);

}

ITERA TE:{

UPDA TE state

SET tsum=tsum+Next,cnt=cnt+1;

}

REVISE:{

UPDA TE state SET

tsum=tsum+Next-SUM(E.Next),

cnt=cnt+1-count(E.*)

FROM EXPIRED AS E;

INSERT INTO RETURN

SELECT tsum/cnt FROM state

}

}

ATLaS also supports a window speci?cation in the FROM clause along the lines proposed in[2].Thus,a win-dow speci?cation consists of:

1.an optional partitioning clause,which partitions the

data into several groups and maintains a separate win-dow for each group,

2.a window size,using either the count of the elements

in the window,or the range of time covered by the window(i.e.,its time-span).

3.an optional?ltering predicate.

Thus,to compute the average call length,but considering only the ten most recent long-distance calls placed by each customer we will write the following query:

Example4Count-Based Window on a Stream

STREAM calls(customer

4Data Mining Applications in ATLaS

Using table functions and recursive aggregates,Algo-rithm1implements a scalable decision tree classi?er using merely14SQL statements.

1:AGGREGA TE classify(iNode Int,RecId Int,iCol Int,

iValue Int,iYorN Int)

2:{T ABLE treenodes(RecId Int,Node Int,Col Int,

Value Int,YorN Int);

3:T ABLE mincol(Col Int);

4:T ABLE summary(Col Int,Value Int,Yc Int,Nc Int)

INDEX(Col,Value);

5:T ABLE ginitable(Col Int,Gini Int);

6:INITIALIZE:ITERA TE:{

7:INSERT INTO treenodes

VALUES(RecId,iNode,iCol,iValue,iYorN);

8:UPDA TE summary

SET Yc=Yc+iYorN,Nc=Nc+1-iYorN

WHERE Col=iCol AND Value=iValue;

9:INSERT INTO summary

SELECT iCol,iValue,iYorN,1-iYorN

WHERE SQLCODE<>0;

}

10:TERMINA TE:{

11:INSERT INTO ginitable

SELECT Col,sum((Yc*Nc)/(Yc+Nc))/sum(Yc+Nc)

FROM summary GROUP BY Col;

HAVING count(Value)>1

AND sum(Yc)>0AND sum(Nc)>0;

12:INSERT INTO mincol

SELECT minpair(Col,Gini)→mPoint

FROM ginitable;

13:INSERT INTO result

SELECT iNode,Col FROM mincol;

{Call classify()recusively to partition each of its}

{subnodes unless it is pure.}

14:SELECT classify(t.Node*MAXV ALUE+m.Value+1,

t.RecId,t.Col,t.Value,t.YorN)

FROM treenodes AS t,

(SELECT tt.RecId RecId,tt.Value Value

FROM treenodes AS tt,mincol AS m

WHERE tt.Col=m.Col)AS m

WHERE t.RecId=m.RecId

GROUP BY m.Value;

}

}

and in-memory table optimization,is carried out during this

step.While ATLaS performs sophisticated local query op-timization,it does not attempt to perform major changes in the overall execution plan,which therefore remains under

programmer’s control.After rewriting,the code generator translates the query graphs into C++code.

The runtime model of ATLaS is based on data pipelin-ing.In particular,all UDAs,including recursive UDAs that

call themselves,are pipelined;thus,tuples inserted into the RETURN relation during the INITIALIZE/ITERATE steps are returned to their caller immediately.Therefore,local tables

declared in a UDA can not reside on the stack.Instead,they are assembled into a state structure which is then passed to the UDA for each INITIALIZE/ITERATE/TERMINATE call,so that these internal data are retained between calls.

The data stream management engine is responsible for

ef?ciently maintaining records in windows and the EX-PIRED table.Records in a window are stored in a disk?le. Count-based windows have?xed sizes,while time-based windows may require dynamic allocation of disk buffers.

A window speci?cation with a PARTITION clause may cor-respond to multiple windows,one for each unique partition key.Records of the windows are clustered by the partition key and stored in a same disk?le.ATLaS also supports data sharing among multiple queries that access the same external data stream concurrently.A single procedure is responsible for reading the data from the external stream and delivering them to the disk buffers of each individ-ual query.Furthermore,window speci?cations of different queries can share disk buffers if the speci?cations have the same?ltering predicate and PARTITION clause.

6About the Demo

Our demonstration consists of the following parts. ATLaS Language Features

The only extension introduced by ATLaS is the ability of de?ning UDAs and table functions in SQL,yet this mini-malist approach makes SQL Turing complete.We demon-strate ATLaS’expressive power and ease-of-use through a suite of UDAs and table functions that implement,for example,temporal databases operators(e.g.coalescing), greedy graph-optimization algorithms(e.g.the shortest path algorithm),OLAP operators(e.g.ROLLUP,CUBE), and many others.

ATLaS provides a GUI IDE for writing ATLaS pro-grams.An ATLaS program may consist of several mod-ules,which are either source code in ATLaS’SQL,source code in C/C++,or libraries to be dynamically linked with other modules.ATLaS also provides query plan visualiza-tion tools so that users can track the effect of query rewrit-ing and optimization.

Continuous Queries on Data Streams

We showcase ATLaS’ability of handling con-tinuous queries using the schema and queries in Stanford’s Stream Query Repository(https://www.wendangku.net/doc/4714049800.html,/stream/sqr).We implement each query in two different approaches.The?rst approach uses ATLaS’windows-on-streams construct similar to that proposed in[2].But ATLaS is Turing complete without this construct.The second approach implements the same query using ATLaS’UDAs and table functions.We will compare the pros and cons of the two approaches.

Data Mining Applications

Substantial extensions have been added to SQL over the years,yet data mining applications remain an unsolved challenge for DBMSs.We demonstrate how ATLaS’min-imalist approach attacks this problem.The demonstration package consists of data mining applications such as the de-cision tree classi?er,the Aprior algorithm for association rule mining,and the density-based clustering algorithm DBSCAN.All applications are written entirely in ATLaS’SQL,and can be applied directly on relational data[6].

Recently,stream data mining has been a?eld of intense research.The demonstration also includes a stream classi-?er that handles time-changing drifts in the streaming data. It is realized by leveraging ATLaS’continuous-query and data mining ability.

Our demonstration will show ATLaS incurs only a mod-est performance overhead with respect to the same applica-tions written in C/C++.

The ATLaS System

The demonstration will reveal ATLaS’internal architecture by focusing on several of its key components,such as the query rewrite module,the query plan generation module, and the stream data management engine. References

[1]https://www.wendangku.net/doc/4714049800.html,/atlas

[2]Brian Babcock,Shivnath Babu,Rajeev Motwani,

and Jennifer Widom.“Models and Issues in Data

Streaming Systems”,SIGMOD Record,V ol.30No.

3,pp.109-120(September2001).

[3]J.M.Hellerstein,P.J.Haas,and H.J.Wang.“On-

line Aggregation”.SIGMOD,1997.

[4]Haixun Wang and Carlo Zaniolo.“Using SQL to

Build New Aggregates and Extenders for Object-

Relational Systems”.VLDB2000.

[5]Haixun Wang and Carlo Zaniolo.“Extending SQL

for Decision Support Applications”.DMDW2002.

[6]Haixun Wang and Carlo Zaniolo.“ATLaS:A Na-

tive Extension of SQL for Data Mining and Stream

Computations”.SIAM Data Mining,May2003.

[7]Haixun Wang and Carlo Zaniolo.“On the Proper-

ties of a Native Extension of SQL for Data Streams

and Data Mining”.Submitted to VLDB2003.

1 Introduction On

On choice-o?ering imperatives Maria Aloni? 1Introduction The law of propositional logic that states the deducibility of either A or B from A is not valid for imperatives(Ross’s paradox,cf.[9]).The command (or request,advice,etc.)in(1a)does not imply(1a)(unless it is taken in its alternative-presenting sense),otherwise when told the former,I would be justi?ed in burning the letter rather then posting it. (1) a.Post this letter!? b.Post this letter or burn it! Intuitively the most natural interpretation of the second imperative is as one presenting a choice between two actions.Following[2](and[6])I call these choice-o?ering imperatives.Another example of a choice-o?ering imperative is (2)with an occurence of Free Choice‘any’which,interestingly,is licensed in this context. (2)Take any card! Like(1a),this imperative should be interpreted as carrying with it a permission that explicates the fact that a choice is being o?ered. Possibility statements behave similarly(see[8]).Sentence(3b)has a read-ing under which it cannot be deduced from(3a),and‘any’is licensed in(4). (3) a.You may post this letter.? b.You may post this letter or burn it. (4)You may take any card. In[1]I presented an analysis of modal expressions which explains the phe-nomena in(3)and(4).That analysis maintains a standard treatment of‘or’as logical disjunction(contra[11])and a Kadmon&Landman style analysis of‘any’as existential quanti?er(contra[3]and[4])assuming,however,an in-dependently motivated‘Hamblin analysis’for∨and?as introducing sets of alternative propositions.Modal expressions are treated as operators over sets of propositional alternatives.In this way,since their interpretation can depend on the alternatives introduced by‘or’(∨)or‘any’(?)in their scope,we can account for the free choice e?ect which arises in sentences like(3b)or(4).In this article I would like to extend this analysis to imperatives.The resulting theory will allow a uni?ed account of the phenomena in(1)-(4).We will start by presenting our‘alternative’analysis for inde?nites and disjunction. ?ILLC-Department of Philosophy,University of Amsterdam,NL,e-mail:M.D.Aloni@uva.nl

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making a self-introduction 作自我介绍 1. may i introduce myself 2. hello, i’m hanson smith. 3. excuse me, i don’t think we’ve met. my name’s hanson smith. 4. how do you do? i’m hanson smith. 5. i’m david anderson. i don’t believe i’ve had the pleasure. 6. first let me introduce myself. i’m peter white, production manager. 7. my name is david. i work in the marketing department. after being introduced. 被介绍与对方认识后. 1. i’m glad to meet you. 很高兴认识你. 2. nice meeting you. 很高兴认识你. (平时用得最多的是nice to meet you ) 3. how nice to meet you. 认识你真高兴. 4. i’ve heard so much about you. 我知道很多关于你的事儿. 5. helen has told me all about you. 海伦对我将了好多你的事儿. 6. i’ve been wanting to meet you for some time.

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④发现异常情况,应立即停机,并切断所有电源。 ⑤周围环境中不存在易燃、易爆、有毒和有腐蚀性气体。 ⑥维修或调整机构之前,必须停机卸压,并切断电源。 (3)空压机检查电动机转向 接好电气线路之后,首先应检查电动机转向。方法如下: 如果是仪表监控器机型,将选择按钮置“手动”档,按下“启动”按钮,紧接着按下“停机”按钮,让电动机试转一下。若从电动机后部看过去,传动轴是顺时针转动,则转向正确。若转向不正确,则断开电源,交换任意两根火线,接好后再试一次。电机与压缩机之间的接筒上有以箭头指明转向。如果是电脑监控器机型,在监控器控制面板上,拉出紧急停机按钮,按“I”键(启动),紧接着马上按“O”键(停机)让电机试转一下。若从电动机后看过去,传动是顺时

针转动,则转向正确。若转向不正确,则断开电源,交换任意两根火线,接好后再试一次。电机与压缩机之间的接筒上有一箭头指明转向。 (4)空压机初次启动前的准备工作 ①检查电气接线是否可靠; ②检查油位,应在油试镜中心线附近,不足则加; ③打开排气阀; ④接通电源; ⑤检查电机的转向; ⑥再次启动压缩机,并缓缓关闭排气截止阀,检查机组的卸载压力是否与设定值一致。若不一致,应重新设定。 ⑦检查各系统工作是否正常,是否有漏油、漏气等现象,是否油不正常的声音,如有异常,应停机检查。 ⑧停机停机前,线关闭排气截止阀,让压缩机在卸载状态下至少运行30秒,然后再停机。

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