Scalable Audience Reach Estimation in Real-time Online Advertising

Online advertising has been introduced as one of the most efficient methods of advertising throughout the recent years. Yet, advertisers are concerned about the efficiency of their online advertising campaigns and consequently, would like to restrict their ad impressions to certain websites and/or certain groups of audience. These restrictions, known as targeting criteria, limit the reachability for better performance. This trade-off between reachability and performance illustrates a need for a forecasting system that can quickly predict/estimate (with good accuracy) this trade-off. Designing such a system is challenging due to (a) the huge amount of data to process, and, (b) the need for fast and accurate estimates. In this paper, we propose a distributed fault tolerant system that can generate such estimates fast with good accuracy. The main idea is to keep a small representative sample in memory across multiple machines and formulate the forecasting problem as queries against the sample. The key challenge is to find the best strata across the past data, perform multivariate stratified sampling while ensuring fuzzy fall-back to cover the small minorities. Our results show a significant improvement over the uniform and simple stratified sampling strategies which are currently widely used in the industry

A Clustering-Based Algorithm for Data Reduction

Finding an efficient data reduction method for large-scale problems is an imperative task. In this paper, we propose a similarity-based self-constructing fuzzy clustering algorithm to do the sampling of instances for the classification task. Instances that are similar to each other are grouped into the same cluster. When all the instances have been fed in, a number of clusters are formed automatically. Then the statistical mean for each cluster will be regarded as representing all the instances covered in the cluster. This approach has two advantages. One is that it can be faster and uses less storage memory. The other is that the number of new representative instances need not be specified in advance by the user. Experiments on real-world datasets show that our method can run faster and obtain better reduction rate than other methods

Techniques for improving clustering and association rules mining from very large transactional databases

Clustering and association rules mining are two core data mining tasks that have been actively studied by data mining community for nearly two decades. Though many clustering and association rules mining algorithms have been developed, no algorithm is better than others on all aspects, such as accuracy, efficiency, scalability, adaptability and memory usage. While more efficient and effective algorithms need to be developed for handling the large-scale and complex stored datasets, emerging applications where data takes the form of streams pose new challenges for the data mining community. The existing techniques and algorithms for static stored databases cannot be applied to the data streams directly. They need to be extended or modified, or new methods need to be developed to process the data streams.In this thesis, algorithms have been developed for improving efficiency and accuracy of clustering and association rules mining on very large, high dimensional, high cardinality, sparse transactional databases and data streams.A new similarity measure suitable for clustering transactional data is defined and an incremental clustering algorithm, INCLUS, is proposed using this similarity measure. The algorithm only scans the database once and produces clusters based on the user’s expectations of similarities between transactions in a cluster, which is controlled by the user input parameters, a similarity threshold and a support threshold. Intensive testing has been performed to evaluate the effectiveness, efficiency, scalability and order insensitiveness of the algorithm.To extend INCLUS for transactional data streams, an equal-width time window model and an elastic time window model are proposed that allow mining of clustering changes in evolving data streams. The minimal width of the window is determined by the minimum clustering granularity for a particular application. Two algorithms, CluStream_EQ and CluStream_EL, based on the equal-width window model and the elastic window model respectively, are developed by incorporating these models into INCLUS. Each algorithm consists of an online micro-clustering component and an offline macro-clustering component. The online component writes summary statistics of a data stream to the disk, and the offline components uses those summaries and other user input to discover changes in a data stream. The effectiveness and scalability of the algorithms are evaluated by experiments.This thesis also looks into sampling techniques that can improve efficiency of mining association rules in a very large transactional database. The sample size is derived based on the binomial distribution and central limit theorem. The sample size used is smaller than that based on Chernoff Bounds, but still provides the same approximation guarantees. The accuracy of the proposed sampling approach is theoretically analyzed and its effectiveness is experimentally evaluated on both dense and sparse datasets.Applications of stratified sampling for association rules mining is also explored in this thesis. The database is first partitioned into strata based on the length of transactions, and simple random sampling is then performed on each stratum. The total sample size is determined by a formula derived in this thesis and the sample size for each stratum is proportionate to the size of the stratum. The accuracy of transaction size based stratified sampling is experimentally compared with that of random sampling.The thesis concludes with a summary of significant contributions and some pointers for further work

Multi-Label Classification Using Higher-Order Label Clusters

Multi-label classification (MLC) is one of the major classification approaches in the context of data mining where each instance in the dataset is annotated with a set of labels. The nature of multiple labels associated with one instance often demands higher computational power compared to conventional single-label classification tasks. A multi-label classification is often simplified by decomposing the task into single-label classification which ignores correlations among labels. Incorporating label correlations into classification task can be hard since correlations may be missing, or may exist among a pair or a large subset of labels. In this study, a novel MLC approach is introduced called Multi-Label Classification with Label Clusters (MLC–LC), which incorporates label correlations into a multi-label learning task using label clusters. MLC–LC uses the well-known Cover-coefficient based Clustering Methodology (C3M) to partition the set of labels into clusters and then employs either the binary relevance or the label powerset method to learn a classifier for each label cluster independently. A test instance is given to each of the classifiers and the label predictions are unioned to obtain a multi-label assignment. The C3M method is especially suited for constructing label clusters since the number of clusters appropriate for a label set as well the initial cluster seeds are automatically computed from the data set. The predictive of MLC–LC is compared with many of the matured and well known multi-label classification techniques on a wide variety of data sets. In all experimental settings, MLC–LC outperformed the other algorithms

An Efficient Fuzzy Clustering-Based Approach for Intrusion Detection

The need to increase accuracy in detecting sophisticated cyber attacks poses a great challenge not only to the research community but also to corporations. So far, many approaches have been proposed to cope with this threat. Among them, data mining has brought on remarkable contributions to the intrusion detection problem. However, the generalization ability of data mining-based methods remains limited, and hence detecting sophisticated attacks remains a tough task. In this thread, we present a novel method based on both clustering and classification for developing an efficient intrusion detection system (IDS). The key idea is to take useful information exploited from fuzzy clustering into account for the process of building an IDS. To this aim, we first present cornerstones to construct additional cluster features for a training set. Then, we come up with an algorithm to generate an IDS based on such cluster features and the original input features. Finally, we experimentally prove that our method outperforms several well-known methods.Comment: 15th East-European Conference on Advances and Databases and Information Systems (ADBIS 11), Vienna : Austria (2011

VerdictDB: Universalizing Approximate Query Processing

Despite 25 years of research in academia, approximate query processing (AQP) has had little industrial adoption. One of the major causes of this slow adoption is the reluctance of traditional vendors to make radical changes to their legacy codebases, and the preoccupation of newer vendors (e.g., SQL-on-Hadoop products) with implementing standard features. Additionally, the few AQP engines that are available are each tied to a specific platform and require users to completely abandon their existing databases---an unrealistic expectation given the infancy of the AQP technology. Therefore, we argue that a universal solution is needed: a database-agnostic approximation engine that will widen the reach of this emerging technology across various platforms. Our proposal, called VerdictDB, uses a middleware architecture that requires no changes to the backend database, and thus, can work with all off-the-shelf engines. Operating at the driver-level, VerdictDB intercepts analytical queries issued to the database and rewrites them into another query that, if executed by any standard relational engine, will yield sufficient information for computing an approximate answer. VerdictDB uses the returned result set to compute an approximate answer and error estimates, which are then passed on to the user or application. However, lack of access to the query execution layer introduces significant challenges in terms of generality, correctness, and efficiency. This paper shows how VerdictDB overcomes these challenges and delivers up to 171$\times$ speedup (18.45$\times$ on average) for a variety of existing engines, such as Impala, Spark SQL, and Amazon Redshift, while incurring less than 2.6% relative error. VerdictDB is open-sourced under Apache License.Comment: Extended technical report of the paper that appeared in Proceedings of the 2018 International Conference on Management of Data, pp. 1461-1476. ACM, 201