package com.hurence.logisland.processor;

import com.hurence.logisland.event.Event;
import com.hurence.logisland.log.LogParser;
import com.hurence.logisland.log.LogParserException;

/**
 */
public class OutlierProcessor implements EventProcessor {

    /**
     * take a line of csv and convert it to a NetworkFlow
     *
     * @param s
     * @return
     */
    public Collection<Event> process(Collection<Event> events) {
    
    }
}

@@ -110,80 +110,880 @@
 and.
-log.LogParse
+event.EventProcesso
 r;%0A
+%0A
 import 
-com.hurence.logisland.log.LogParserException;%0A%0A/**
+java.util.Collection;%0A%0A/**%0A * Outlier Analysis: A Hybrid Approach%0A *%0A * In order to function at scale, a two-phase approach is taken%0A *%0A * - For every data point%0A *      - Detect outlier candidates using a robust estimator of variability (e.g. median absolute deviation) that uses distributional sketching (e.g. Q-trees)%0A *      - Gather a biased sample (biased by recency)%0A *      - Extremely deterministic in space and cheap in computation%0A *%0A * - For every outlier candidate%0A *      - Use traditional, more computationally complex approaches to outlier analysis (e.g. Robust PCA) on the biased sample%0A *      - Expensive computationally, but run infrequently%0A *%0A * This becomes a data filter which can be attached to a timeseries data stream within a distributed computational framework (i.e. Storm, Spark, Flink, NiFi) to detect outliers.
 %0A */
@@ -1128,18 +1128,16 @@
 * @param
- s
 %0A     * 
@@ -1217,20 +1217,37 @@
 ents) %7B%0A
+%0A
     
+    return null;
 %0A    %7D%0A%7D

fix outlier interface