Low-cost management of inverted files for online full-text search

In dynamic environments with frequent content updates, we re-quire online full-text search that scales to large data collections and achieves low search latency. Several recent methods that support fast incremental indexing of documents typically keep on disk mul-tiple partial index structures that they continuously update as new documents are added. However, spreading indexing information across multiple locations on disk tends to considerably decrease the search responsiveness of the system. In the present paper, we take a fresh look at the problem of online full-text search with consid-eration of the architectural features of modern systems. Selective Range Flush is a greedy method that we introduce to manage the index in the system by using fixed-size blocks to organize the data on disk and dynamically keep low the cost of data transfer between memory and disk. As we experimentally demonstrate with the Pro-teus prototype implementation that we developed, we retrieve in-dexing information at latency that matches the lowest achieved by existing methods. Additionally, we reduce the total building cost by 30 % in comparison to methods with similar retrieval time

Scalable Architecture for Integrated Batch and Streaming Analysis of Big Data

Thesis (Ph.D.) - Indiana University, Computer Sciences, 2015As Big Data processing problems evolve, many modern applications demonstrate special characteristics. Data exists in the form of both large historical datasets and high-speed real-time streams, and many analysis pipelines require integrated parallel batch processing and stream processing. Despite the large size of the whole dataset, most analyses focus on specific subsets according to certain criteria. Correspondingly, integrated support for efficient queries and post- query analysis is required. To address the system-level requirements brought by such characteristics, this dissertation proposes a scalable architecture for integrated queries, batch analysis, and streaming analysis of Big Data in the cloud. We verify its effectiveness using a representative application domain - social media data analysis - and tackle related research challenges emerging from each module of the architecture by integrating and extending multiple state-of-the-art Big Data storage and processing systems. In the storage layer, we reveal that existing text indexing techniques do not work well for the unique queries of social data, which put constraints on both textual content and social context. To address this issue, we propose a flexible indexing framework over NoSQL databases to support fully customizable index structures, which can embed necessary social context information for efficient queries. The batch analysis module demonstrates that analysis workflows consist of multiple algorithms with different computation and communication patterns, which are suitable for different processing frameworks. To achieve efficient workflows, we build an integrated analysis stack based on YARN, and make novel use of customized indices in developing sophisticated analysis algorithms. In the streaming analysis module, the high-dimensional data representation of social media streams poses special challenges to the problem of parallel stream clustering. Due to the sparsity of the high-dimensional data, traditional synchronization method becomes expensive and severely impacts the scalability of the algorithm. Therefore, we design a novel strategy that broadcasts the incremental changes rather than the whole centroids of the clusters to achieve scalable parallel stream clustering algorithms. Performance tests using real applications show that our solutions for parallel data loading/indexing, queries, analysis tasks, and stream clustering all significantly outperform implementations using current state-of-the-art technologies