mirror of
https://github.com/apache/zeppelin
synced 2026-05-24 09:38:26 +00:00
Load data directly from http without using filesystem
This commit is contained in:
Lee moon soo
parent
3f87f448a4
commit
653b1bc47f
1 changed files with 51 additions and 50 deletions
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@ -13,7 +13,7 @@
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"groups": [],
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"scatter": {}
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"editorHide": false
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"editorHide": true
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"settings": {
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"params": {},
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@ -32,42 +32,9 @@
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"status": "FINISHED",
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"progressUpdateIntervalMs": 500
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},
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{
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"title": "Prepare data",
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"text": "import sys.process._\n//you will need \u0027wget\u0027 tool to download\n\"wget http://archive.ics.uci.edu/ml/machine-learning-databases/00222/bank.zip\" !\n\"mkdir data\" !\n\"unzip bank.zip -d data\" !\n\"rm bank.zip\" !",
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"config": {
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"colWidth": 12.0,
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"graph": {
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"mode": "table",
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"height": 300.0,
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"optionOpen": false,
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"keys": [],
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"values": [],
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"groups": [],
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"scatter": {}
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},
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"title": true
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},
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"settings": {
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"params": {},
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"forms": {}
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},
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"jobName": "paragraph_1417656535623_-196593192",
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"id": "20141204-102855_1590713432",
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"result": {
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"code": "SUCCESS",
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"type": "TEXT",
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"msg": "import sys.process._\nwarning: there were 1 feature warning(s); re-run with -feature for details\nres1: Int \u003d 0\nwarning: there were 1 feature warning(s); re-run with -feature for details\nres2: Int \u003d 0\nwarning: there were 1 feature warning(s); re-run with -feature for details\nres3: Int \u003d 0\nwarning: there were 1 feature warning(s); re-run with -feature for details\nres4: Int \u003d 0\n"
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},
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"dateCreated": "Dec 4, 2014 10:28:55 AM",
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"dateStarted": "Apr 1, 2015 9:11:12 PM",
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"dateFinished": "Apr 1, 2015 9:11:22 PM",
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"status": "FINISHED",
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"progressUpdateIntervalMs": 500
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},
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{
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"title": "Load data into table",
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"text": "import sys.process._\n// Zeppelin creates and injects sc (SparkContext) and sqlContext (HiveContext or SqlContext)\n// So you don\u0027t need create them manually\n\nval zeppelinHome \u003d (\"pwd\" !!).replace(\"\\n\", \"\")\nval bankText \u003d sc.textFile(s\"file://$zeppelinHome/data/bank-full.csv\")\n\ncase class Bank(age: Integer, job: String, marital: String, education: String, balance: Integer)\n\nval bank \u003d bankText.map(s \u003d\u003e s.split(\";\")).filter(s \u003d\u003e s(0) !\u003d \"\\\"age\\\"\").map(\n s \u003d\u003e Bank(s(0).toInt, \n s(1).replaceAll(\"\\\"\", \"\"),\n s(2).replaceAll(\"\\\"\", \"\"),\n s(3).replaceAll(\"\\\"\", \"\"),\n s(5).replaceAll(\"\\\"\", \"\").toInt\n )\n).toDF()\nbank.registerTempTable(\"bank\")\n\n",
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"text": "import org.apache.commons.io.IOUtils\nimport java.net.URL\nimport java.nio.charset.Charset\n\n// Zeppelin creates and injects sc (SparkContext) and sqlContext (HiveContext or SqlContext)\n// So you don\u0027t need create them manually\n\n// load bank data\nval bankText \u003d sc.parallelize(\n IOUtils.toString(\n new URL(\"https://s3.amazonaws.com/apache-zeppelin/tutorial/bank/bank.csv\"),\n Charset.forName(\"utf8\")).split(\"\\n\"))\n\ncase class Bank(age: Integer, job: String, marital: String, education: String, balance: Integer)\n\nval bank \u003d bankText.map(s \u003d\u003e s.split(\";\")).filter(s \u003d\u003e s(0) !\u003d \"\\\"age\\\"\").map(\n s \u003d\u003e Bank(s(0).toInt, \n s(1).replaceAll(\"\\\"\", \"\"),\n s(2).replaceAll(\"\\\"\", \"\"),\n s(3).replaceAll(\"\\\"\", \"\"),\n s(5).replaceAll(\"\\\"\", \"\").toInt\n )\n).toDF()\nbank.registerTempTable(\"bank\")",
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"config": {
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"colWidth": 12.0,
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"graph": {
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@ -90,11 +57,11 @@
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"result": {
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"code": "SUCCESS",
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"type": "TEXT",
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"msg": "import sys.process._\nsqlContext: org.apache.spark.sql.SQLContext \u003d org.apache.spark.sql.SQLContext@2c91e2d6\nwarning: there were 1 feature warning(s); re-run with -feature for details\nzeppelinHome: String \u003d /home/langley/lab/incubator-zeppelin\nbankText: org.apache.spark.rdd.RDD[String] \u003d /home/langley/lab/incubator-zeppelin/data/bank-full.csv MapPartitionsRDD[1] at textFile at \u003cconsole\u003e:31\ndefined class Bank\nbank: org.apache.spark.sql.DataFrame \u003d [age: int, job: string, marital: string, education: string, balance: int]\n"
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"msg": "import org.apache.commons.io.IOUtils\nimport java.net.URL\nimport java.nio.charset.Charset\nbankText: org.apache.spark.rdd.RDD[String] \u003d ParallelCollectionRDD[32] at parallelize at \u003cconsole\u003e:65\ndefined class Bank\nbank: org.apache.spark.sql.DataFrame \u003d [age: int, job: string, marital: string, education: string, balance: int]\n"
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},
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"dateCreated": "Feb 10, 2015 1:52:59 AM",
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"dateStarted": "Apr 1, 2015 9:11:28 PM",
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"dateFinished": "Apr 1, 2015 9:11:39 PM",
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"dateStarted": "Jul 3, 2015 1:43:40 PM",
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"dateFinished": "Jul 3, 2015 1:43:45 PM",
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"status": "FINISHED",
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"progressUpdateIntervalMs": 500
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},
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@ -144,11 +111,11 @@
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"result": {
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"code": "SUCCESS",
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"type": "TABLE",
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"msg": "age\tvalue\n18\t12\n19\t35\n20\t50\n21\t79\n22\t129\n23\t202\n24\t302\n25\t527\n26\t805\n27\t909\n28\t1038\n29\t1185\n"
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"msg": "age\tvalue\n19\t4\n20\t3\n21\t7\n22\t9\n23\t20\n24\t24\n25\t44\n26\t77\n27\t94\n28\t103\n29\t97\n"
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},
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"dateCreated": "Feb 10, 2015 1:53:02 AM",
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"dateStarted": "Apr 1, 2015 9:11:43 PM",
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"dateFinished": "Apr 1, 2015 9:11:45 PM",
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"dateStarted": "Jul 3, 2015 1:43:17 PM",
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"dateFinished": "Jul 3, 2015 1:43:23 PM",
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"status": "FINISHED",
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"progressUpdateIntervalMs": 500
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},
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@ -206,11 +173,11 @@
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"result": {
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"code": "SUCCESS",
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"type": "TABLE",
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"msg": "age\tvalue\n18\t12\n19\t35\n20\t50\n21\t79\n22\t129\n23\t202\n24\t302\n25\t527\n26\t805\n27\t909\n28\t1038\n29\t1185\n30\t1757\n31\t1996\n32\t2085\n33\t1972\n34\t1930\n"
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"msg": "age\tvalue\n19\t4\n20\t3\n21\t7\n22\t9\n23\t20\n24\t24\n25\t44\n26\t77\n27\t94\n28\t103\n29\t97\n30\t150\n31\t199\n32\t224\n33\t186\n34\t231\n"
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},
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"dateCreated": "Feb 12, 2015 2:54:04 PM",
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"dateStarted": "Apr 1, 2015 9:12:03 PM",
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"dateFinished": "Apr 1, 2015 9:12:03 PM",
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"dateStarted": "Jul 3, 2015 1:43:28 PM",
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"dateFinished": "Jul 3, 2015 1:43:29 PM",
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"status": "FINISHED",
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"progressUpdateIntervalMs": 500
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},
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@ -279,11 +246,11 @@
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"result": {
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"code": "SUCCESS",
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"type": "TABLE",
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"msg": "age\tvalue\n18\t12\n19\t35\n20\t47\n21\t74\n22\t120\n23\t175\n24\t248\n25\t423\n26\t615\n27\t658\n28\t697\n29\t683\n30\t1012\n31\t1017\n32\t941\n33\t746\n34\t650\n35\t631\n36\t538\n37\t453\n38\t394\n39\t346\n40\t257\n41\t241\n42\t218\n43\t183\n44\t170\n45\t146\n46\t130\n47\t100\n48\t124\n49\t101\n50\t76\n51\t72\n52\t62\n53\t71\n54\t55\n55\t54\n56\t45\n57\t38\n58\t35\n59\t36\n60\t27\n61\t5\n63\t2\n66\t5\n67\t3\n68\t4\n69\t2\n70\t1\n71\t1\n72\t5\n73\t2\n77\t1\n83\t2\n86\t1\n"
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"msg": "age\tvalue\n19\t4\n20\t3\n21\t7\n22\t9\n23\t17\n24\t13\n25\t33\n26\t56\n27\t64\n28\t78\n29\t56\n30\t92\n31\t86\n32\t105\n33\t61\n34\t75\n35\t46\n36\t50\n37\t43\n38\t44\n39\t30\n40\t25\n41\t19\n42\t23\n43\t21\n44\t20\n45\t15\n46\t14\n47\t12\n48\t12\n49\t11\n50\t8\n51\t6\n52\t9\n53\t4\n55\t3\n56\t3\n57\t2\n58\t7\n59\t2\n60\t5\n66\t2\n69\t1\n"
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},
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"dateCreated": "Feb 13, 2015 11:04:22 PM",
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"dateStarted": "Apr 1, 2015 9:12:10 PM",
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"dateFinished": "Apr 1, 2015 9:12:10 PM",
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"dateStarted": "Jul 3, 2015 1:43:33 PM",
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"dateFinished": "Jul 3, 2015 1:43:34 PM",
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"status": "FINISHED",
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"progressUpdateIntervalMs": 500
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},
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@ -299,7 +266,8 @@
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"values": [],
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"groups": [],
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"scatter": {}
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}
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},
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"editorHide": true
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},
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"settings": {
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"params": {},
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@ -319,22 +287,55 @@
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"progressUpdateIntervalMs": 500
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},
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{
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"config": {},
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"text": "%md\n\nAbout bank data\n\n```\nCitation Request:\n This dataset is public available for research. The details are described in [Moro et al., 2011]. \n Please include this citation if you plan to use this database:\n\n [Moro et al., 2011] S. Moro, R. Laureano and P. Cortez. Using Data Mining for Bank Direct Marketing: An Application of the CRISP-DM Methodology. \n In P. Novais et al. (Eds.), Proceedings of the European Simulation and Modelling Conference - ESM\u00272011, pp. 117-121, Guimarães, Portugal, October, 2011. EUROSIS.\n\n Available at: [pdf] http://hdl.handle.net/1822/14838\n [bib] http://www3.dsi.uminho.pt/pcortez/bib/2011-esm-1.txt\n```",
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"config": {
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"colWidth": 12.0,
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"graph": {
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"mode": "table",
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"height": 300.0,
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"optionOpen": false,
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"keys": [],
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"values": [],
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"groups": [],
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"scatter": {}
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},
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"editorHide": true
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},
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"settings": {
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"params": {},
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"forms": {}
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},
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"jobName": "paragraph_1427420818407_872443482",
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"id": "20150326-214658_12335843",
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"result": {
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"code": "SUCCESS",
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"type": "HTML",
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"msg": "\u003cp\u003eAbout bank data\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eCitation Request:\n This dataset is public available for research. The details are described in [Moro et al., 2011]. \n Please include this citation if you plan to use this database:\n\n [Moro et al., 2011] S. Moro, R. Laureano and P. Cortez. Using Data Mining for Bank Direct Marketing: An Application of the CRISP-DM Methodology. \n In P. Novais et al. (Eds.), Proceedings of the European Simulation and Modelling Conference - ESM\u00272011, pp. 117-121, Guimarães, Portugal, October, 2011. EUROSIS.\n\n Available at: [pdf] http://hdl.handle.net/1822/14838\n [bib] http://www3.dsi.uminho.pt/pcortez/bib/2011-esm-1.txt\n\u003c/code\u003e\u003c/pre\u003e\n"
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},
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"dateCreated": "Mar 26, 2015 9:46:58 PM",
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"dateStarted": "Jul 3, 2015 1:44:56 PM",
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"dateFinished": "Jul 3, 2015 1:44:56 PM",
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"status": "FINISHED",
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"progressUpdateIntervalMs": 500
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},
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{
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"config": {},
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"settings": {
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"params": {},
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"forms": {}
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},
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"jobName": "paragraph_1435955447812_-158639899",
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"id": "20150703-133047_853701097",
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"dateCreated": "Jul 3, 2015 1:30:47 PM",
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"status": "READY",
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"progressUpdateIntervalMs": 500
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}
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],
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"name": "Zeppelin Tutorial",
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"id": "2A94M5J1Z",
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"angularObjects": {},
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"config": {
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"looknfeel": "default"
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},
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"info": {}
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}
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}
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