SUMMARY: Fungi form a large and heterogeneous group of eukaryotic organisms with diverse ecological niches. The high importance of fungi contrasts with our limited understanding of fungal lifestyle and adaptability to environment. Over the last decade, the high-throughput sequencing technology produced tremendous RNA-sequencing (RNA-seq) data. However, there is no comprehensive database for mycologists to conveniently explore fungal gene expression and alternative splicing. Here, we have developed FungiExp, an online database including 35 821 curated RNA-seq samples derived from 220 fungal species, together with gene expression and alternative splicing profiles. It allows users to query and visualize gene expression and alternative splicing in the collected RNA-seq samples. Furthermore, FungiExp contains several online analysis tools, such as differential/specific, co-expression network and cross-species gene expression conservation analysis. Through these tools, users can obtain new insights by re-analyzing public RNA-seq data or upload personal data to co-analyze with public RNA-seq data. AVAILABILITY AND IMPLEMENTATION: The FungiExp is freely available at https://bioinfo.njau.edu.cn/fungiExp. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

This paper showcases some of the newly introduced parallel execution methods in Oracle RDBMS. These methods provide highly scalable and adaptive evaluation for the most commonly used SQL operations - joins, group-by, rollup/cube, grouping sets, and window functions. The novelty of these techniques is their use of multi-stage parallelization models, accommodation of optimizer mistakes, and the runtime parallelization and data distribution decisions. These parallel plans adapt based on the statistics gathered on the real data at query execution time. We realized enormous performance gains from these adaptive parallelization techniques. The paper also discusses our approach to parallelize queries with operations that are inherently serial. We believe all these techniques will make their way into big data analytics and other massively parallel database systems.

Execution plans for SQL statements have a significant impact on the overall performance of database systems. New optimizer statistics, configuration parameter changes, software upgrades and hardware resource utilization are among a multitude of factors that may cause the query optimizer to generate new plans. While most of these plan changes are beneficial or benign, a few rogue plans can potentially wreak havoc on system performance or availability, affecting critical and time-sensitive business application needs. The normally desirable ability of a query optimizer to adapt to system changes may sometimes cause it to pick a sub-optimal plan compromising the stability of the system. In this paper, we present the new SQL Plan Management feature in Oracle 11g. It provides a comprehensive solution for managing plan changes to provide stable and optimal performance for a set of SQL statements. Two of its most important goals are preventing sub-optimal plans from being executed while allowing new plans to be used if they are verifiably better than previous plans. This feature is tightly integrated with Oracle's query optimizer. SQL Plan Management is available to users via both command-line and graphical interfaces. We describe the feature and then, using an industrial-strength application suite, present experimental results that show that SQL Plan Management provides stable and optimal performance for SQL statements with no performance regressions.

Main memory is a critical resource when processing long-running queries over data streams with state intensive operators. In this work, we investigate state spill strategies that handle run-time memory shortage when processing such complex queries by selectively pushing operator states into disks. Unlike previous solutions which all focus on one single operator only, we instead target queries with multiple state intensive operators. We observe an interdependency among multiple operators in the query plan when spilling operator states. We illustrate that existing strategies, which do not take account of this interdependency, become largely ineffective in this query context. Clearly, a consolidated plan level spill strategy must be devised to address this problem. Several data spill strategies are proposed in this paper to maximize the run-time query throughput in memory constrained environments. The bottom-up state spill strategy is an operator-level strategy that treats all data in one operator state equally. More sophisticated partition-level data spill strategies are then proposed to take different characteristics of the input data into account, including the local output, the global output and the global output with penalty strategies. All proposed state spill strategies have been implemented in the D-CAPE continuous query system. The experimental results confirm the effectiveness of our proposed strategies. In particular, the global output strategy and the global output with penalty strategy have shown favorable results as compared to the other two more localized strategies.

Middleware enables distributed components to interact with each others in diverse and complex manners. Such interactions should be modeled at architecture level for controlling the complexity of incorporating middleware into the target system. This paper extends a traditional architectural description language for describing the diverse and complex interactions enabled by middleware as complex connectors and constraints on them in a model driven process. Such functions and qualities of connectors that satisfy the requirements of the target system are modeled without any consideration of middleware at first. Then the connectors and constraints on them are refined by the characteristics induced by middleware. All information of connectors produced in the two-step process can be described at three levels, including the connection, coordination and context. The language and process are illustrated and evaluated by applying them into J2EE (Java 2 Platform Enterprise Edition) applications.

In this paper, an approach is presented to formally model architecture based software development process. The ability of UML in modeling software architecture is reinforced by defining a generic model of component and software architecture, and by integrating the model with UML class model and interaction model to unify software development process. In UML, software development is modeled in different views. With formal semantics, designer can keep consistency among these views. The paper demonstrates how to use the method to construct software.

Adaptive operator scheduling algorithms for continuous query processing are usually designed to serve a single performance objective, such as minimizing memory usage or maximizing query throughput. We observe that different performance objectives may sometimes conflict with each other. Also due to the dynamic nature of streaming environments, the performance objective may need to change dynamically. Furthermore, the performance specification defined by users may itself be multi-dimensional. Therefore, utilizing a single scheduling algorithm optimized for a single objective is no longer sufficient. In this paper, we propose a novel adaptive scheduling algorithm selection framework named AMoS. It is able to leverage the strengths of existing scheduling algorithms to meet multiple performance objectives. AMoS employs a lightweight learning mechanism to assess the effectiveness of each algorithm. The learned knowledge can be used to select the algorithm that probabilistically has the best chance of improving the performance. In addition, AMoS has the flexibility to add and adapt to new scheduling algorithms, query plans and data sets during execution. Our experimental results show that AMoS significantly outperforms the existing scheduling algorithms with regard to satisfying both uni-objective and multi-objective performance requirements.

Reflective middleware is proposed for guaranteeing desired qualities of middleware based systems which reside in the extremely open and dynamic Internet. Current researches and practices focus on how to monitor and change the whole system through reflective mechanisms provided by middleware. However, they put little attention on why, when and what to monitor and change because it is very hard for middleware to collect enough knowledge which is usually specific to the whole system. Being an important artifact in software development, software architecture records plentiful design information, especially the considerations for quality attributes of the target system. It is a natural idea to provide reflective middleware with enough knowledge via software architecture. This paper presents a demonstration of the idea. In this demonstration, the self-adaptations can be analyzed in a quality attribute scenario based way and specified by an extended architecture description language. Such knowledge prescribed at the design phase can be used directly by an architecture based reflective middleware which then automatically adapts itself at runtime.

Dynamic plan migration is concerned with the on-the-fly transition from one continuous query plan to a semantically equivalent yet more efficient plan. Migration is important for stream monitoring systems where long-running queries may have to withstand fluctuations in stream workloads and data characteristics. Existing migration methods generally adopt a pause-drain-resume strategy that pauses the processing of new data, purges all old data in the existing plan, until finally the new plan can be plugged into the system. However, these existing strategies do not address the problem of migrating query plans that contain stateful operators, such as joins. We now develop solutions for online plan migration for continuous stateful plans. In particular, in this paper, we propose two alternative strategies, called the moving state strategy and the parallel track strategy, one exploiting reusability and the second employs parallelism to seamlessly migrate between continuous join plans without affecting the results of the query. We develop cost models for both migration strategies to analytically compare them. We embed these migration strategies into the CAPE [7], a prototype system of a stream query engine, and conduct a comparative experimental study to evaluate these two strategies for window-based join plans. Our experimental results illustrate that the two strategies can vary significantly in terms of output rates and intermediate storage spaces given distinct system configurations and stream workloads.

A quantitative approach is developed for modeling and analyzing objectively the perceptual performance of computer-communication networks and computer systems that involve real-time human interaction and communication with real-time streamed signals such as audio, speech, or video. The proposed 'perceptual analysis' method is based on augmenting traditional system performance models with the incorporation of ITU-T recommended objective perceptual quality assessment methods such as PEAQ (perceived audio quality) or PESQ (perceptual evaluation of speech quality). The approach provides an avenue for including the quantitative evaluation of perceptual quality measures, such as MOS (mean opinion score), in system performance modeling. The analysis of a voice-over-IP network access link model with G.711 and G.729 multiplexed calls is presented to demonstrate a perceptual teletraffic engineering application.