Slide 1

CHAO ZHANG MONGODB WITH DLVHEX

Slide 2

THE MONGODB

Slide 3

INTRODUCTION TO MONGODB MongoDB is an open-source document database which provides high performance on big data. A record in MongoDB is a document, which is similar to JSON format, called BSON.

Slide 4

ADVANTAGE OF MONGODB JSON is native data type in many programming languages. Embedded documents and arrays instead of joins. Dynamic schema.

Slide 5

EMBEDDED VS NORMALIZED

Slide 6

MONGODB IS FAST AND SCALABLE Disk seeking and data locality

Slide 7

RELATIONSHIP WITH RDB

Slide 8

WORKING WITH MONGODB In the MongoDB we have the following data: { "name" : "Joe", "age" : 33, "gender" : "m" }, { "name" : "Jane", "age" : 40, "gender" : "f" }, { "name" : "Marry", "age" : 33, "gender" : "f" }, { "name" : "Methuselah", "age" : null }, { "name" : "Samantha", "age" : 21, "city" : "Los Angeles", "state" : "CA", "gender" : "f" }, { "name" : "Chris", "age" : 25, "gender" : "m" }, { "name" : "Mike", "age" : 34, "marriage" : true, "gender" : "m" }, { "name" : "Robot", "age" : 21, "birthday" : ISODate("2013-10- 02T01:11:53.976Z"), "gender" : "m" }, { "name" : "Josh", "age" : 29, "grades" : [ 90, 89, 85, 65, 88 ], "gender" : "m" }

Slide 9

INSERT DATA INTO MONGODB 1. Use Command: db.persons.insert() for one document every time. 2. Or Create a variable: var doc = [{},{},{}] db.persons.insert(doc) for multiple records

Slide 10

QUERY THE MONGODB Find all the person, whose age is less than 30, return the name. db.persons.find({age:{$lt:30}},{name:1,age:1, _id:0}) { "name" : "Samantha", "age" : 21 } { "name" : "Chris", "age" : 25 } { "name" : "Robot", "age" : 21 } { "name" : "Josh", "age" : 29 } Query Operation Output Operation Collection name & command

Slide 11

QUERY WITH ARRAY Find the grades of Josh db.persons.find({name:"Josh"},{grades:1,_id:0}) { "grades" : [ 90, 89, 85, 65, 88 ] }

Slide 12

EXAMPLE SENSOR DATA { "@id": "smart_ontology:record001", "@type": ["smart_ontology:Record"], "smart_ontology:startTime": ["1410114600", "2014-09-07T11:30:00-07:00], "smart_ontology:endTime": ["1410116400", "2014-09-07T12:00:00-07:00"], "smart_ontology:fromDevice": [ { "@id": "smart_ontology:phone1", "@type": ["smart_ontology:SmartPhone"], "smart_ontology:installedIn": [ { "@id": "smart_ontology:Alice", "@type": ["smart_ontology:User"] } ] } ], "smart_ontology:isValue": [ "run", "run", "run", "walk", "walk", "walk", "walk", "stay", "stay", "stay", "walk", "stay", "stay", "stay", "stay", "stay", "stay", "stay", "stay", "stay", "stay", "stay", "stay", "stay", "stay", "stay", "stay", "walk", "walk", "walk" ] }

Slide 13

SENSOR DATA FROM BASIS

Slide 14

SLEEP DATA FROM BASIS

Slide 15

DLVHEX MONGODB PLUGIN

Slide 16

PLUGIN MODEL: - QUERY (1) &mongo_query[D, Q, P](Output) - D: database and collection name: eg: test.persons - Q: query criteria: eg: {age: {$lt: 30} - P: query result projection: eg: {name;gender} (should be separated by ;) result(NAME,GENDER) :- &mongo_query[">

Slide 17

PLUGIN MODEL: - QUERY(2) ARRAY If the mongo_query result is array, you should run mongo_array plugin to traverse the data in array. &mongo_array[A](Index, Value) - A: the input of an Array - Output: Index: the index number of one array item Value: the value of one array item result(NAME,GRADE) :- &mongo_query["test.persons","{grades:{$exists:1}}","name;grades "](NAME, GRADE). grade(NAME,I,G) :- &mongo_array[GRADE](I,G), result(NAME,GRADE),I=0.

Slide 18

PLUGIN MODEL: - INSERT &mongo_insert[D, K1, V1,[K2,V2]...]() - D: database and collection name: eg: test.persons - K: The field of JSON Element: eg: name - V: The value of JSON Element: eg: adam - The [K, V] should be paired, and can have as many as you want. success :- &mongo_insert["test.persons","name","adam","age","2 0","interest","CS"]().

Slide 19

EXAMPLE OF FALLING REASONING(1) %%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Part 1: Data Retrive from MongoDB %%%%%%%%%%%%%%%%%%%%%%%%%%%%% % % The event of Falling has been detected % %%%%%%%%%%%%%%%%%%%%%%%%%%%%% falling_time(T) :- &mongo_query["smart.example", "{'smart_ontology:isValue.0': 'fall'}", "smart_ontology:isTime.0"](T). thirty_minutes_before(T1) :- falling_time(T), T1 = T-1800. hour_before(T2) :- falling_time(T), T2 = T-3600. one_minute_before(T3) :- falling_time(T), T3 = T - 60.

Slide 20

EXAMPLE OF FALLING REASONING(2) %%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Get the movement data from smartphone sensor in the range % of 30 minutes before the falling down happened. %%%%%%%%%%%%%%%%%%%%%%%%%%%%% movement_array(S,A) :- &mongo_query["smart.example", "{'smart_ontology:fromDevice.0.@id':'smart_ontology:phone1', 'smart_ontology:startTime.0': {$lt: '$INPUT'}, 'smart_ontology:endTime.0': {$gt: '$INPUT'}}", "smart_ontology:isValue;smart_ontology:startTime.0", T,T](A,S), falling_time(T).

Slide 21 T, thirty_minutes_before(T).">

EXAMPLE OF FALLING REASONING(3) movement_array(S,A) :- &mongo_query["smart.example", "{'smart_ontology:fromDevice.0.@id':'smart_ontology:phone1', 'smart_ontology:startTime.0': {$lt: '$INPUT'}, 'smart_ontology:endTime.0': {$gt: '$INPUT'}}", "smart_ontology:isValue;smart_ontology:startTime.0", T1,T1](A,S), thirty_minutes_before(T1). movement_total(S,I,V) :- &mongo_array[A](I,V), movement_array(S,A), V != "NULL. movement_in_30_minutes(T1,V) :- movement_total(S,I,V), T1= S+I1, I1 = I*60, T1 > T, thirty_minutes_before(T).

Slide 22

EXAMPLE OF FALLING REASONING(4) %%%%%%%%%%%%%%%%%%%%%%%%%%%%% % % Get the "insulin taken" and "eating" event data from % camera sensor in one hour before the falling down happened. % %%%%%%%%%%%%%%%%%%%%%%%%%%%%% take_insulin_time(T) :- &mongo_query["smart.example", "{'smart_ontology:fromDevice.0.@type.0':'smart_ontology:Camera', 'smart_ontology:isValue.0':'take_insulin', 'smart_ontology:isTime.0': {$lt: '$INPUT'}, 'smart_ontology:isTime.0': {$gt: '$INPUT'}}}", "smart_ontology:isTime.0", T1, T2](T), falling_time(T1), hour_before(T2).

Slide 23 T1.">

EXAMPLE OF FALLING REASONING(5) eat_time(T) :- &mongo_query["smart.example", "{'smart_ontology:fromDevice.0.@type.0':'smart_ontology:Camera', 'smart_ontology:isValue.0':'eat','smart_ontology:isTime.0': {$lt: '$INPUT'}, 'smart_ontology:isTime.0': {$gt: '$INPUT'}}}", "smart_ontology:isTime.0", T1, T2](T), falling_time(T1), hour_before(T2). eat_after_insulin_taken :- take_insulin_time(T1), eat_time(T), T > T1.

Slide 24

EXAMPLE OF FALLING REASONING(6) %%%%%%%%%%%%%%%%%%%%%%%%%%%%% % % Glucose testing was given every hour % %%%%%%%%%%%%%%%%%%%%%%%%%%%%% glucose(T,V) :- &mongo_query["smart.example", "{'smart_ontology:fromDevice.0.@id':'smart_ontology:glucose1','smart_ontology:isTime.0': {$lt: '$INPUT'}, 'smart_ontology:isTime.0': {$gt: '$INPUT'}}}", "smart_ontology:isTime.0;smart_ontology:isValue.0", T1, T2](T,V), falling_time(T1), hour_before(T2).

Slide 25

EXAMPLE OF FALLING REASONING(7) %%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Get the heart rate 1 minute before the falling down event %%%%%%%%%%%%%%%%%%%%%%%%%%%%% heart_rate_array(A) :- &mongo_query["smart.example", "{'smart_ontology:fromDevice.0.@id':'smart_ontology:heartrate1', 'smart_ontology:startTime.0': {$lt: '$INPUT'}, 'smart_ontology:endTime.0': {$gt: '$INPUT'}}", "smart_ontology:isValue", T,T](A), one_minute_before(T). heart_rate(I,V) :- &mongo_array[A](I,V), heart_rate_array(A), V != "NULL". not_newest_heart_rate(I,V) :- heart_rate(I,V), heart_rate(I1,V1), I < I1. newest_heart_rate(V) :- heart_rate(I,V), not not_newest_heart_rate(I,V).

Slide 26 50.">

EXAMPLE OF FALLING REASONING(8) %%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Part 2: Reasoning Task %%%%%%%%%%%%%%%%%%%%%%%%%%%%% % If the activities of run is dominant in the previous 30 % minutes, the person has been activiated intensively. That % might be one reason of falling down. %%%%%%%%%%%%%%%%%%%%%%%%%%%%% intensive_activity_possibility(Y1) :- &count[movement_in_30_minutes](X1), &count[movement_in_30_minutes,mask,"run"](X2), Y = X2*100, Y1 = Y/X1. reason_of_falling("intensive_activity") :- intensive_activity_possibility(Y), Y > 50.

Slide 27

EXAMPLE OF FALLING REASONING(9) %%%%%%%%%%%%%%%%%%%%%%%%%%%%% % % If the person has low glucose level and not eat after the % insulin taking, or the use has two insulin taking in the % previous hour, the reason of falling might be "insulin shock" % %%%%%%%%%%%%%%%%%%%%%%%%%%%%% reason_of_falling("insulin_shock") :- glucose(T,V), V< 50, not eat_after_insulin_taken. reason_of_falling("insulin_shock") :- glucose(T,V), V = 2.

Slide 28

EXAMPLE OF FALLING REASONING(10) %%%%%%%%%%%%%%%%%%%%%%%%%%%%% % If the person has a heart attack that the heart almost stop % beating, the falling down reason would be "heart attack %%%%%%%%%%%%%%%%%%%%%%%%%%%%% reason_of_falling("heart_attack") :- newest_heart_rate(V), V < 20. %%%%%%%%%%%%%%%%%%%%%%%%%%%%% % % Insert the reasoning result back to the database % %%%%%%%%%%%%%%%%%%%%%%%%%%%%% insert_success:- &mongo_insert["smart.reasoning", "reason_of_falling", X](), reason_of_falling(X).

Slide 29

HOMEWORK 1. Insert yourself into the MongoDB in test.persons. You can put your name, your gender, and anything else you like. 2. Check the data in the MongoDB in basis.sensor, and run a dlvhex mongo-query to return the step data in a day, which is not 0. The output should look like this: {records("steps",1402619100,76),records("steps",1402619040,41),records("steps",1402618980,4),records("steps",14026189 20,24) }