In the present era, where competition pervades across all domains, profitability holds crucial economic importance for numerous companies, including the banking industry. Offering the right products to customers is a fundamental problem that directly affects banks’ net revenue. Machine learning (ML) approaches can address this issue using customer behavior analysis from historical customer data. This study addresses the issue by processing customer transactions using a bank’s current account debt (CAD) product with state‐of‐the‐art ML approaches. In the first step, exploratory data analysis (EDA) is performed to examine the data and detect patterns and anomalies. Then, different regression methods (tree‐based methods) are tested to analyze the model’s performance. The obtained results show that the light gradient boosting machine (LGBM) algorithm outperforms other methods with an 84% accuracy rate in the light gradient boosting algorithm, which is the most accurate of the three methods used.

Summary The Global Database of Event, Language, and Tone (GDELT) is the only global political georeferenced event dataset with more than 250 million observations covering all countries in the world since January 1, 1979. TABARI and CAMEO are the tools that are used to collect and code events from all international news coverage. To query such big geospatial data, traditional RDBMS can no longer be used, and the need for parallel distributed solutions has become a necessity. MapReduce paradigm has proven to be a scalable platform to process and analyze Big Data in the cloud. Hadoop, as an implementation of MapReduce, is an open‐source application that has been widely used and accepted in academia and industry. However, when dealing with Spatial Data, Hadoop is not equipped well and does not perform efficiently. SpatialHadoop is an extension of Hadoop with the support of spatial data. In this paper, we present Geographic Information System Query and Analytics Framework (GISQAF), which has been built on top of SpatialHadoop. GISQAF focuses on two parts: query processing and data analytics. For the query processing part, we show how this solution outperforms Hadoop query processing by orders of magnitude when applying queries on the GDELT dataset with a size of 60 GB. We show the results for various types of queries. For the data analytics part, we present an approach for finding Spatial co‐occurring events. We show how GISQAF is suitable and efficient to handle data analytics techniques. Copyright © 2015 John Wiley & Sons, Ltd.

Summary Anomaly detection refers to the identification of patterns in a dataset that do not conform to expected patterns. Such non‐conformant patterns typically correspond to samples of interest and are assigned to different labels in different domains, such as outliers, anomalies, exceptions, and malware. A daunting challenge is to detect anomalies in rapid voluminous streams of data. This paper presents a novel, generic real‐time distributed anomaly detection framework for multi‐source stream data. As a case study, we investigate anomaly detection for a multi‐source VMware‐based cloud data center, which maintains a large number of virtual machines (VMs). This framework continuously monitors VMware performance stream data related to CPU statistics (e.g., load and usage). It collects data simultaneously from all of the VMs connected to the network and notifies the resource manager to reschedule its CPU resources dynamically when it identifies any abnormal behavior from its collected data. A semi‐supervised clustering technique is used to build a model from benign training data only. During testing, if a data instance deviates significantly from the model, then it is flagged as an anomaly. Effective anomaly detection in this case demands a distributed framework with high throughput and low latency. Distributed streaming frameworks like Apache Storm, Apache Spark, S4, and others are designed for a lower data processing time and a higher throughput than standard centralized frameworks. We have experimentally compared the average processing latency of a tuple during clustering and prediction in both Spark and Storm and demonstrated that Spark processes a tuple much quicker than storm on average. Copyright © 2016 John Wiley & Sons, Ltd.

Streaming data is one of the attention receiving sources for concept-evolution studies. When a new class occurs in the data stream it can be considered as a new concept and so the concept-evolution. One attractive problem occurring in the concept-evolution studies is the recurring classes from our previous study. In data streams, a class can disappear and reappear after a while. Existing studies on data stream classification techniques either misclassify the recurring class or falsely identify the recurring classes as novel classes. Because of the misclassification or false novel classification, the error rates increases on those studies. In this paper we address the problem by defining a novel ensemble technique “class-based” ensemble which replaces the traditional “chunk-based” approach in order to detect the recurring classes. We discuss the details of two different approaches in class-based ensemble and explain and compare them in detail. Different than the previous studies in the field, we also prove the superiority of both “class-based” ensemble method over state-of-art techniques via empirical approach on a number of benchmark data sets including Web comments as text mining challenge.

Collecting observations from all international news coverage and using TABARI software to code events, the Global Database of Event, Language, and Tone (GDELT) is the only global political georeferenced event dataset with 250+ million observations covering all countries in the world from January 1, 1979 to the present with daily updates. The purpose of this widely used dataset is to help understand and uncover spatial, temporal and perceptual trends and behaviors of the social and international system. To query such big geospatial data, traditional RDBMS can no longer be used and the need for parallel distributed solutions has become a necessity. MapReduce paradigm has proved to be a scalable platform to process and analyze Big Data in the cloud. Hadoop as an implementation of MapReduce is an open source application that has been widely used and accepted in academia and industry. However, when dealing with Spatial Data, Hadoop is not equipped well and falls short as it doesn't perform efficiently in terms of running time. SpatialHadoop is an extension of Hadoop with the support of spatial data. In this paper, we present Geographic Information System Querying Framework (GISQF) to process Massive Spatial Data. This framework has been built on top of the open source SpatialHadoop system which exploits two-layer spatial indexing techniques to speed up query processing. We show how this solution outperforms Hadoop query processing by orders of magnitude when applying queries on GDELT dataset with a size of 60 GB. We show the results for three types of queries, Longitude-Latitude Point queries, Circle-Area queries, and Aggregation queries.

Aim of this study is applying the ensemble classification methods over the stock market closing values, which can be assumed as time series and finding out the relation between the economy news. In order to keep the study back ground clear, the majority voting method has been applied over the three classification algorithms, which are the k-nearest neighborhood, support vector machine and the C4.5 tree. The results gathered from two different feature extraction methods are correlated with majority voting meta classifier (ensemble method) which is running over three classifiers. The results show the success rates are increased after the ensemble at least 2 to 3 percent success rate.