빅데이터 환경에서 기업의 의사결정에 필요한 DW 시스템은 더욱 중요해졌고, 대용량 데이터 분석은 필수가 되었다. 전통적인 DBMS의 확장성, 성능 한계를 해결하기 위해서 소프트웨어 뿐만 아니라 최신의 하드웨어 디바이스와 결합하여 어플라이언스 형태의 DW 구축이 대세가 되고 있는 환경에서, 국산 DBMS의 선두주자 티맥스소프트는 외산 DB 어플라이언스와 경쟁할 수 있는 데이터베이스 어플라이언스를 출시하였다. 최근 HP 하드웨어와 어플라이언스 협력 모델을 내놓았으며, 기존의 DBMS가 해결하지 못한 초대용량과 고성능, 그리고 데이터의 확장성이 특징이다. ZetaData는 고성능 데이터베이스 서버와 지능형 스토리지 서버, 초고속 네트워크를 통해 대용량 데이터의 빠른 처리와 시스템 안정성을 제공하는 통합(Consolidated) 데이터 솔루션이다.

1. 빅데이터본부 김병곤
fharenheit@ex-em.com

7. 탐침 차량 정보(PVD) 설명
VIN 차량식별번호
utcTime 수집시각
longitude 경도
latitude 위도
elevation 고도
heading 방향
speed 속도
vehicleType 차종
Safety Extension
events
HazardLights 비상등
StopLineViolation 정지선위반
Emergency Response 긴급 차량
HardBraking 급브레이크
LightsChanged 깜빡이 변경
FlatTire 타이어 압 부족
DisabledVehicle 엔진 정지
extevents
GetOnAndDown 승하차
Accident 사고
Trouble 이상
HardDeceleration 급감속
HardStop 급정지
HardTurn 급회전
UTurn 유턴

8. 27310015^A20160105141139^A20160105141137^AC3999RSU00552^A9220^A4^A127.259445^A36.512460^A98.900000^A193.200000^A26^A2^A0^A0^A0^A0^A0^A0^A0^A0
^A0^A0^A0^A0^A0^A0^A0^A0^A0^A0^A0^A0^A27.000000^A1^A0^A8.000000^ACITSOBE-0006^A^A^A^A^A
27310015^A20160105141141^A20160105141139^AC3999RSU00552^A9220^A4^A127.259426^A36.512345^A98.900000^A193.950000^A30^A2^A0^A0^A0^A0^A0^A0^A0^A0
^A0^A0^A0^A0^A0^A0^A0^A0^A0^A0^A0^A0^A32.000000^A1^A0^A4.000000^ACITSOBE-0006^A^A^A^A^A
27310015^A20160105141142^A20160105141140^AC3999RSU00552^A9220^A4^A127.259406^A36.512230^A96.100000^A194.250000^A36^A2^A0^A0^A0^A0^A0^A0^A0^A0
^A0^A0^A0^A0^A0^A0^A0^A0^A0^A0^A0^A0^A34.000000^A1^A0^A3.000000^ACITSOBE-0006^A^A^A^A^A
27310015^A20160105141143^A20160105141141^AC3999RSU00552^A9220^A4^A127.259388^A36.512155^A94.200000^A194.562500^A40^A2^A0^A0^A0^A0^A0^A0^A0^A0
^A0^A0^A0^A0^A0^A0^A0^A0^A0^A0^A0^A0^A12.000000^A1^A0^A4.000000^ACITSOBE-0006^A^A^A^A^A
27310015^A20160105141145^A20160105141143^AC3999RSU00552^A9220^A4^A127.259353^A36.511984^A90.200000^A194.862500^A54^A2^A0^A0^A0^A0^A0^A0^A0^A0
^A0^A0^A0^A0^A0^A0^A0^A0^A0^A0^A0^A0^A36.000000^A1^A0^A10.000000^ACITSOBE-0006^A^A^A^A^A
27310015^A20160105141146^A20160105141144^AC3999RSU00552^A9220^A4^A127.259323^A36.511865^A89.300000^A194.862500^A49^A2^A0^A0^A0^A0^A0^A0^A0^A0
^A0^A0^A0^A0^A0^A0^A0^A0^A0^A0^A0^A0^A20.000000^A1^A0^A0.000000^ACITSOBE-0006^A^A^A^A^A
27310015^A20160105141147^A20160105141145^AC3999RSU00552^A9220^A4^A127.259276^A36.511652^A89.900000^A195.400000^A56^A2^A0^A0^A0^A0^A0^A0^A0^A0
^A0^A0^A0^A0^A0^A0^A0^A0^A0^A0^A0^A0^A1.000000^A1^A0^A2.000000^ACITSOBE-0006^A^A^A^A^A
27310015^A20160105141148^A20160105141146^AC3999RSU00552^A9220^A4^A127.259264^A36.511568^A90.500000^A194.700000^A56^A2^A0^A0^A0^A0^A0^A0^A0^A0
^A0^A0^A0^A0^A0^A0^A0^A0^A0^A0^A0^A0^A9.000000^A1^A0^A0.000000^ACITSOBE-0006^A^A^A^A^A
27310015^A20160105141149^A20160105141147^AC3999RSU00552^A9220^A4^A127.259226^A36.511374^A91.600000^A194.737500^A56^A2^A0^A0^A0^A0^A0^A0^A0^A0
^A0^A0^A0^A0^A0^A0^A0^A0^A0^A0^A0^A0^A15.000000^A1^A0^A0.000000^ACITSOBE-0006^A^A^A^A^A
27310015^A20160105141150^A20160105141148^AC3999RSU00552^A9220^A4^A127.259188^A36.511236^A91.900000^A195.437500^A56^A2^A0^A0^A0^A0^A0^A0^A0^A0
^A0^A0^A0^A0^A0^A0^A0^A0^A0^A0^A0^A0^A15.000000^A1^A0^A0.000000^ACITSOBE-0006^A^A^A^A^A

19. <?xml version="1.0" encoding="UTF-8"?>
<commonPlatform>
<dataType>device</dataType>
<serviceId>10000001</serviceId>
<serviceTypeId>st</serviceTypeId>
<deviceTypeId>ACC</deviceTypeId>
<deviceId>st_000001</deviceId>
<custom>
<![CDATA[
{
"TI": "st_2015_0925_121210",
"UI1": "honggildong1",
"UI2": "honggildong1",
"CI1": "honggildong1",
"CI2": "honggildong1",
"TST": "2015-12-25 13:01:15.0",
"TET": "2015-12-25 13:01:15.0"
}
]]]>
</custom>
<data>
<![CDATA[
{
"DATA": [
{
"VL": "10",
"PATH_X": "10",
"PATH_Y": "10",
"DT": "YYYY-MM-DD HH:MM:SS.fff"
},
{
"VL": "10",
"PATH_X": "10",
"PATH_Y": "10",
"DT": "YYYY-MM-DD HH:MM:SS.fff"
},
{
"VL": "10",
"PATH_X": "10",
"PATH_Y": "10",
"DT": "YYYY-MM-DD HH:MM:SS.fff"
}
]
}
]]]>
</data>
</commonPlatform>

20. bigdata@iot1:~ $> hadoop fs -ls
/raw/iot/GP00000000000000000001/st/st_000001/st_000001/2015/11/07
-rw-r—r-- 3 bigdata hdfs 6352 2015-11-07 11:37
/raw/iot/GP00000000000000000001/st/st_000001/st_000001/2015/11/07/st_000001.144689682908
0
-rw-r—r-- 3 bigdata hdfs 6352 2015-11-07 11:38
/raw/iot/GP00000000000000000001/st/st_000001/st_000001/2015/11/07/st_000001.144689686011
1
-rw-r—r-- 3 bigdata hdfs 6352 2015-11-07 11:38
/raw/iot/GP00000000000000000001/st/st_000001/st_000001/2015/11/07/st_000001.144689689214
8
-rw-r—r-- 3 bigdata hdfs 5955 2015-11-07 11:39
/raw/iot/GP00000000000000000001/st/st_000001/st_000001/2015/11/07/st_000001.144689692418
8
-rw-r--r-- 3 bigdata hdfs 6352 2015-11-07 11:39
/raw/iot/GP00000000000000000001/st/st_000001/st_000001/2015/11/07/st_000001.144689695431
6
-rw-r--r-- 3 bigdata hdfs 6352 2015-11-07 11:40
/raw/iot/GP00000000000000000001/st/st_000001/st_000001/2015/11/07/st_000001.144689698621
4
-rw-r--r-- 3 bigdata hdfs 6352 2015-11-07 11:40
/raw/iot/GP00000000000000000001/st/st_000001/st_000001/2015/11/07/st_000001.144689701821

27. // 예측모델 로딩
PMML pmml = loadModel("minwon.pmml");
// 예측모델 평가
ModelEvaluatorFactory modelEvaluatorFactory =
ModelEvaluatorFactory.newInstance();
Evaluator evaluator = modelEvaluatorFactory.newModelManager(pmml);
evaluator.verify();
// 예측모델의 입출력
List<FieldName> activeFields = evaluator.getActiveFields();
List<FieldName> outputFields = evaluator.getOutputFields();
// 민원 내용 입력
Map<FieldName, Object> input = new HashMap();
input.put(activeFields.get(0), "백수오"); // 식품유형
input.put(activeFields.get(1), "이엽우피소"); // 키워드
Map<FieldName, ?> evaluated = evaluator.evaluate(input);
// 예측결과
System.out.println(evaluated.get(outputFields.get(0))); // 불량식품유형
System.out.println(evaluated.get(outputFields.get(1))); // 판단불가 확률
System.out.println(evaluated.get(outputFields.get(2))); // 판단가능 확률