Deletion based view maintenance is a building block in many query explanation and analytics applications, such as sensitivity analysis, what-if analysis, data cleaning, and probabilistic databases, which require exploring how a query's output result changes when excluding (deleting) subsets of input relations (referred to as “deletion interventions”). These applications' ability to remain interactive is limited by how fast they can recompute output results, especially over complex queries and large data. A critical limitation in existing engines is their performance. To remain interactive, existing approaches either restrict the types of aggregation functions, require explanations to be pre-specified, or apply sampling and approximation. We show how to build an interactive deletion intervention evaluation engine for SPJA queries, by Ieveraging recent advances in fast fine-grained provenance that captures input/output relationship for each physical operator in a denormalized compact pointer-free format. We use provenance to propagate the deletion status of tuples per operator, in a tight loop that leads to improvement in instruction and data locality. In addition, this representation is amenable to a variety of physical optimizations, such as dictionary-encoding, bit-packing, vectorization, and parallelization. Operators' output tuples can be evenly split across threads, and their deletion status can be computed independently. Our preliminary results show that on the TPC-H workload, our engine takes on average 9ms to evaluate 1K deletion interventions at a time, achieving on average wins of 3 orders of magnitude over DBToaster, a state-of-the art IVM based engine, and wins of 4 orders of magnitude against existing provenance based approaches. Utilizing tight loops and a cache-friendly provenance and intervention representation enable us to evaluate thousands of deletion interventions at interactive speed, and enable interactive time query explanations over more complex queries and data than previously possible.

What-if queries are the building blocks for many explanation and analytics applications—sensitivity analysis, hypothetical reasoning, data cleaning, probabilistic databases—that explore how a query's output changes due to input data changes. Their response time is bounded by intervention evaluation latency, which can be in the minute or hours for complex queries and large datasets. FaDE is a compilation engine that uses provenance to evaluate hypothetical deletion and scaling interventions at low latency and high throughput. FaDE forgoes conventional provenance representations as symbolic expressions and leverages their underlying relational structure. This accelerates intervention evaluation on average by 1000× against IVM and 10,000× against prior provenance-based approaches. In addition, FaDE develops a suite of optimizations (e.g., compilation, parallelization, incremental evaluation, sparse representations) that collectively raise evaluation throughput to >1 million interventions per sec—a rate that can brute-force existing applications within 1 s.

Interactive data visualization and exploration (DVE) applications, such as the one in Figure 1, have rapidly grown in popularity with use cases in numerous sectors [2, 4, 9, 11, 15]. Like typical web services, DVE applications may be run on heterogeneous client devices and networks, with users expecting fast response times under 100 ms [12]. However, the resource demands of DVE applications are magnified and highly unpredictable, making it difficult to achieve such interactivity.

With the rapid increase in raw volume data sizes, such as terabyte-sized microscopy volumes, the corresponding segmentation label volumes have become extremely large as well. We focus on integer label data, whose efficient representation in memory, as well as fast random data access, pose an even greater challenge than the raw image data. Often, it is crucial to be able to rapidly identify which segments are located where, whether for empty space skipping for fast rendering, or for spatial proximity queries. We refer to this process as culling. In order to enable efficient culling of millions of labeled segments, we present a novel hybrid approach that combines deterministic and probabilistic representations of label data in a data-adaptive hierarchical data structure that we call the label list tree. In each node, we adaptively encode label data using either a probabilistic constant-time access representation for fast conservative culling, or a deterministic logarithmic-time access representation for exact queries. We choose the best data structures for representing the labels of each spatial region while building the label list tree. At run time, we further employ a novel query-adaptive culling strategy. While filtering a query down the tree, we prune it successively, and in each node adaptively select the representation that is best suited for evaluating the pruned query, depending on its size. We show an analysis of the efficiency of our approach with several large data sets from connectomics, including a brain scan with more than 13 million labeled segments, and compare our method to conventional culling approaches. Our approach achieves significant reductions in storage size as well as faster query times.

This paper presents Abstractocyte, a system for the visual analysis of astrocytes and their relation to neurons, in nanoscale volumes of brain tissue. Astrocytes are glial cells, i.e., non-neuronal cells that support neurons and the nervous system. The study of astrocytes has immense potential for understanding brain function. However, their complex and widely-branching structure requires high-resolution electron microscopy imaging and makes visualization and analysis challenging. Furthermore, the structure and function of astrocytes is very different from neurons, and therefore requires the development of new visualization and analysis tools. With Abstractocyte, biologists can explore the morphology of astrocytes using various visual abstraction levels, while simultaneously analyzing neighboring neurons and their connectivity. We define a novel, conceptual 2D abstraction space for jointly visualizing astrocytes and neurons. Neuroscientists can choose a specific joint visualization as a point in this space. Interactively moving this point allows them to smoothly transition between different abstraction levels in an intuitive manner. In contrast to simply switching between different visualizations, this preserves the visual context and correlations throughout the transition. Users can smoothly navigate from concrete, highly-detailed 3D views to simplified and abstracted 2D views. In addition to investigating astrocytes, neurons, and their relationships, we enable the interactive analysis of the distribution of glycogen, which is of high importance to neuroscientists. We describe the design of Abstractocyte, and present three case studies in which neuroscientists have successfully used our system to assess astrocytic coverage of synapses, glycogen distribution in relation to synapses, and astrocytic-mitochondria coverage.

The controller is a critical piece of the SDN architecture, where it is considered as the mastermind of SDN networks. Thus, its failure will cause a significant portion of the network to fail. Overload is one of the common causes of failure since the controller is frequently invoked by new flows. Even through SDN controllers are often replicated, the significant recovery time can be an overkill for the availability of the entire network. In order to overcome the problem of the overloaded controller failure in SDN, this paper proposes a novel controller offload solution for failure mitigation based on a prediction module that anticipates the presence of a harmful long-term load. In fact, the long-standing load would eventually overwhelm the controller leading to a possible failure. To predict whether the load in the controller is short-term or long-term load, we used three different classification algorithms: Support Vector Machine, k-Nearest Neighbors, and Naive Bayes. Our evaluation results demonstrate that Support Vector Machine algorithm is applicable for detecting the type of load with an accuracy of 97.93% in a real-time scenario. Besides, our scheme succeeded to offload the controller by switching between the reactive and proactive mode in response to the prediction module output.