Network communication is the slowest component of many operators in distributed parallel databases deployed for large-scale analytics. Whereas considerable work has focused on speeding up databases on modern hardware, communication reduction has received less attention. Existing parallel DBMSs rely on algorithms designed for disks with minor modifications for networks. A more complicated algorithm may burden the CPUs but could avoid redundant transfers of tuples across the network. We introduce track join, a new distributed join algorithm that minimizes network traffic by generating an optimal transfer schedule for each distinct join key. Track join extends the trade-off options between CPU and network. Track join explicitly detects and exploits locality, also allowing for advanced placement of tuples beyond hash partitioning on a single attribute. We propose a novel data placement algorithm based on track join that minimizes the total network cost of multiple joins across different dimensions in an analytical workload. Our evaluation shows that track join outperforms hash join on the most expensive queries of real workloads regarding both network traffic and execution time. Finally, we show that our data placement optimization approach is both robust and effective in minimizing the total network cost of joins in analytical workloads.

The air distribution characteristics formed by an "air curtain" ventilation approach are investigated in detail. The airflow visualization and full-scale experimental results of air distribution in an occupied zone are reported in this paper. The Coanda effect of air curtain ventilation and the spreading airflow over the floor in a room are demonstrated. Additionally, the "air lake" or "air pool" phenomenon created by air curtain ventilation resembles displaced air movement to some extent. An air curtain ventilation approach is regarded as a bridge between mixing flow and displacement flow. In fact, it is a hybrid method of mixing flow and displacement flow. The current experimental study and its results are helpful in understanding a new air distribution method i.e. an "air curtain" used for room ventilation.

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Abstract-The discounted hitting time (DHT), which is a random-walk similarity measure for graph node pairs, is useful in various applications, including link prediction, collaborative recommendation, and reputation ranking. We examine a novel query, called the multi-way join (or n-way join), on DHT scores. Given a graph and n sets of nodes, the n-way join retrieves a set of n-tuples with the k highest scores, according to some aggregation function of DHT values. This query enables analysis and prediction of complex relationship among n sets of nodes. Since an n-way join is expensive to compute, we develop the Partial Join algorithm (or PJ). This solution decomposes an nway join into a number of top-m 2-way joins, and combines their results to construct the answer of the n-way join. Since PJ may necessitate the computation of top-(m + 1) 2-way joins, we study an incremental solution, which allows the top-(m + 1) 2-way join to be derived quickly from the top-m 2-way join results earlier computed. We further examine fast processing and pruning algorithms for 2-way joins. An extensive evaluation on three real datasets shows that PJ accurately evaluates n-way joins, and is four orders of magnitude faster than basic solutions.

Modern database management systems employ sophisticated query optimization techniques that enable the generation of efficient plans for queries over very large data sets. A variety of other applications also process large data sets, but cannot leverage database-style query optimization for their code. We therefore identify an opportunity to enhance an open-source programming language compiler with database-style query optimization. Our system dynamically generates execution plans at query time, and runs those plans on chunks of data at a time. Based on feedback from earlier chunks, alternative plans might be used for later chunks. The compiler extension could be used for a variety of data-intensive applications, allowing all of them to benefit from this class of performance optimizations.