Anomaly detection (AD) is a fundamental task for time-series analytics with important implications for the downstream performance of many applications. In contrast to other domains where AD mainly focuses on point-based anomalies (i.e., outliers in standalone observations), AD for time series is also concerned with range-based anomalies (i.e., outliers spanning multiple observations). Nevertheless, it is common to use traditional point-based information retrieval measures, such as Precision, Recall, and F-score, to assess the quality of methods by thresholding the anomaly score to mark each point as an anomaly or not. However, mapping discrete labels into continuous data introduces unavoidable shortcomings, complicating the evaluation of range-based anomalies. Notably, the choice of evaluation measure may significantly bias the experimental outcome. Despite over six decades of attention, there has never been a large-scale systematic quantitative and qualitative analysis of time-series AD evaluation measures. This paper extensively evaluates quality measures for time-series AD to assess their robustness under noise, misalignments, and different anomaly cardinality ratios. Our results indicate that measures producing quality values independently of a threshold (i.e., AUC-ROC and AUC-PR) are more suitable for time-series AD. Motivated by this observation, we first extend the AUC-based measures to account for range-based anomalies. Then, we introduce a new family of parameter-free and threshold-independent measures, Volume Under the Surface (VUS), to evaluate methods while varying parameters. We also introduce two optimized implementations for VUS that reduce significantly the execution time of the initial implementation. Our findings demonstrate that our four measures are significantly more robust in assessing the quality of time-series AD methods.

Over the past five decades, the relational database model has proven to be a scaleable and adaptable model for querying a variety of structured data, with use cases in analytics, transactions, graphs, streaming and more. However, most of the world's data is unstructured. Thus, despite their success, the reality is that the vast majority of the world's data has remained beyond the reach of relational systems. The rise of deep learning and generative AI offers an opportunity to change this. These models provide a stunning capability to extract semantic understanding from almost any type of document, including text, images, and video, which can extend the reach of databases to all the world's data. In this paper we explore how these new technologies will transform the way we build database management software, creating new that systems that can ingest, store, process, and query all data. Building such systems presents many opportunities and challenges. In this paper we focus on three: scalability, correctness, and reliability, and argue that the declarative programming paradigm that has served relational systems so well offers a path forward in the new world of AI data systems as well. To illustrate this, we describe several examples of such declarative AI systems we have built in document and video processing, and provide a set of research challenges and opportunities to guide research in this exciting area going forward. And lovely apparitions,-dim at first, Then radiant, as the mind arising bright From the embrace of beauty (whence the forms Of which these are the phantoms) casts on them The gathered rays which are reality- Shall visit us the progeny immortal Of Painting, Sculpture, and rapt Poesy, And arts, though unimagined, yet to be; Prometheus Unbound, Percy Bysshe Shelley

In modern cloud environments, ephemeral resources with intermittent availability and fluctuating monetary costs are becoming common. This dynamic nature presents a new challenge when deploying cloud-native databases: adaptive query execution, which can suspend queries when the resources are scarce or costs unexpectedly soar, and then resume them when the resources become available or cost-effective. Addressing this challenge requires the design and implementation of query suspension and resumption with a mechanism that can adaptively determine when, if, and how to suspend queries. In this paper, we propose Riveter, a query suspension and resumption framework that can adaptively pause ongoing queries using various strategies, including (1) a redo strategy that terminates queries and subsequently re-runs them, (2) a pipeline-level strategy that suspends a query once one of its pipelines has completed to reduce the storage requirements for intermediate data, (3) and a process-level strategy that enables the suspension of query execution processes at any given moment but generates a substantial volume of intermediate data for query resumption. We also devise a cost model to estimate query latency using various strategies and an algorithm to select the one that causes minimum latency. To demonstrate the effectiveness of Riveter, we conduct evaluations based on the TPC-H benchmark to investigate intermediate data persistence, strategy selection, and cost model-based estimation. Our results not only present the difference among the strategies of Riveter in terms of the size of persisted intermediate data and the time of triggering the suspension but also confirm the adaptive and efficient query suspension and resumption delivered by Riveter.

Distance measures are core building blocks in time-series analysis and the subject of active research for decades. Unfortunately, the most detailed experimental study in this area is outdated (over a decade old) and, naturally, does not reflect recent progress. Importantly, this study (i) omitted multiple distance measures, including a classic measure in the time-series literature; (ii) considered only a single time-series normalization method; and (iii) reported only raw classification error rates without statistically validating the findings, resulting in or fueling four misconceptions in the time-series literature. Motivated by the aforementioned drawbacks and our curiosity to shed some light on these misconceptions, we comprehensively evaluate 71 time-series distance measures. Specifically, our study includes (i) 8 normalization methods; (ii) 52 lock-step measures; (iii) 4 sliding measures; (iv) 7 elastic measures; (v) 4 kernel functions; and (vi) 4 embedding measures. We extensively evaluate these measures across 128 time-series datasets using rigorous statistical analysis. For the most promising measures, we present an accuracy-to-runtime analysis and summarize recent progress on a generalized lower bounding measure that accelerates all elastic distances. Our findings debunk four long-standing misconceptions that significantly alter the landscape of what is known about existing distance measures. With the new foundations in place, we discuss open challenges and promising directions.

Approximately every five years, a group of database researchers meet to do a self-assessment of our community, including reflections on our impact on the industry as well as challenges facing our research community. This report summarizes the discussion and conclusions of the 9th such meeting, held during October 9-10, 2018 in Seattle.

There are many situations where we need to determine the most likely strategy that another team is following. Their strategy dictates their most likely next actions by selecting the optimal policy from their set of policies. In this scenario, there is a hierarchical, multi-agent, multi-team environment where the teams are built on layers of agents working together at each level to coordinate their behaviors, such as SiMAMT. We can think of a strategy as a hierarchically layered policy network that allows for teams to work together as a group while maintaining their own personalities. They can also shift from one policy to another as the situation dictates. SiMAMT creates an environment like this where sets of such teams can work together as allies or team up against others as adversaries. In this context, we wish to have a set of teams working as allies facing another set of teams as adversaries. One alliance should be able to analyze the actions of another alliance to determine the most likely strategy that they are following, thus predicting their next actions as well as the next best actions for the current alliance. To accomplish this, the algorithm builds graphs that represent the alignment (i.e., the constellation) of the various agent’s policies and their movement dependency diagrams (MDDs). These graphs are a clear way to represent the individual agent’s policies and their aggregation into a strategy. In this instance, the edges of the graphs represent choices that the policy can make while the vertices represent the decision junctures. This creates a map of the various agents as they move through a progression of decisions, where each decision is made at a decision juncture, and each edge shows the probabilistic progression from each of those decisions. These graphs can show the likelihood of actions taken at each level of the hierarchy, thus encoding the behaviors of the agents, their groups, the teams, and the alliances. We wish to demonstrate that these graphs can represent large sets of teams or alliances and that each alliance can use these representations to coordinate their own behavior while analyzing the behaviors of other alliances. Further, we wish to show that an alliance can make a decision in interactive time on which policy from within their strategy set should be in place based on their observations of other alliance’s strategies. To do so, the algorithm will build a probabilistic graph based on the observed actions of the other alliances by observing the actions taken by each agent within that alliance. It can then compare that probabilistic graph with known graph strategies or those that it has learned along the way. We present this methodology and verify it with experimentation confirmed in the conclusions in this paper.

Relaying on the study of the development of agricultural process and mechanisms, system of farming process reforms, protection of farming and agricultural methodology and also by the development in the area of information technology, it became possible to perceive the process of precision agriculture. Comprehensive review of the monitoring systems of the agricultural environment system has been offered in this paper which is based on one of the emerging technologies called Wireless sensor network (WSN). WSN provide processed real time field data from sensors physically distributed in the field . In this paper introduction to the theory of the monitoring system is described along with discussion of the features of hardware components design & software design of the parts composed, topologies of the network, along with communication protocols in addition with present challenges in the area are overviewed.

The end of Moore’s law will push database system designers to be more judicious with computation as the growth in data outpaces the availability of computational resources. Eagerness, or aggressively consuming resources to immediately and quickly complete the task at hand, is one source of wasted resources in modern data systems where the systems expend unnecessary resources waiting on queries, data, or both. Intelligently deferring a task to a later point in time can increase result reuse, reduce work that might later be invalidated, or avoid unnecessary work altogether. We propose a research prototype system, CrocodileDB, which is a resource-efficient database system that automatically optimizes deferment based on user-specification and workload prediction. CrocodileDB integrates new ways of specifying timing information, new query execution policies, new task schedulers, and new data loading schemes.

Abstract: The increasing tendency across scientific disciplines to write multi authored papers [1,2] makes the issue of the sequence of contributors’ names a major topic both in terms of reflecting actual contributions and in a posteriori assessments by evaluation committees. The reviewers aware that there are different cultures to authorship order. The usual and informal practice of giving the whole credit (impact factor) to each author of a multi authored paper is not adequate and over emphasizes the minor contributions of many authors. Similarly, evaluation of authors according to citation frequencies means often overrating resulting from high-impact but multi authored publications. Teja Tscharntke et al. [72] proposed that four methods. Like as SDC,EC, FLAE, and PCI. Comparison of the credit for contributions to this study under the four different models has been suggested. The proposed systems, such as Individual Frequency (IF) and Weighted Frequency (WF), have no repeated impact for each position.

New sources of “big data” such as the Internet, mobile applications, data-driven science and large-scale sensors (IoT) are driving the demand for growing computing performance. The efficient analysis of data in native raw formats in real-time is increasingly important due to rapid data generation, popular demands for analytics and quick insights to derive immediate responses. Traditional data processing systems can deliver high performance on loaded data, but transforming raw data into internal system formats is computationally expensive. Data transformations rather than arithmetic operations dominate the task, and such transformation is a critical barrier to increased data processing performance. We propose a two-part software/hardware approach, namely, the unified transformation accelerator (hardware) and accelerated transformation operators (software), to accelerate data analytics on unloaded raw data and exceed the performance of structured data analytical systems. Together, they enable real-time decision making and fast knowledge exploration on messy, heterogeneous, and ad-hoc raw data. The unified transformation accelerator (UTA) creates new and flexible architecture support for analytical workloads. Exploiting customization and local memory for efficiency and MIMD parallelism for flexibility and performance, we designed a novel hardware architecture, the Unstructured Data Processor (UDP). UDP is a programmable general-purpose data transformation accelerator, customized for data analytics. We completed the instruction set architecture design, the micro-architecture implementation, the software toolchain (compiler, linker, loader), and the simulation infrastructures. The UDP has four unique features for performance and generality: multi-way dispatch, variable-size symbol, flexible-source dispatch, and flexible addressing. Extensive evaluation of data transformation kernels, which ranges from compression to pattern matching, shows UDP achieves 20x average speedup and 1900x energy efficiency when compared with an 8-thread CPU processor. The UDP ASIC implementation is > 100x less power and area than a single CPU core. The accelerated transformation operators (ATO) is a software framework for integrating hardware accelerations into data analytical systems in a pervasive and flexible fashion. It allows arbitrary data representations on query plan edges and arbitrary use of hardware acceleration within any operator implementations. This approach preserves full operator composition and query optimization capabilities, and requires essentially no front end and execution engine changes. We build the ACCORDA (Accelerated Operators for Raw Data Analysis) system, by extending the state-of-art distributed analytical system (SparkSQL) with the ATO approach. We will study the performance benefit of ACCORDA over a traditional analytical system (SparkSQL) using real-world end-to-end query workloads. We expect the unloaded raw data processing speed of the ACCORDA system to match or even exceed SparkSQL with loaded data. Furthermore, we describe the research plan and the full thesis outline in the proposal. The complete experiment results will be presented in the final thesis.

One consistent and growing area of concentration of artificial intelligence is in the area of games — serious games and simulations, educational games, and traditional game AI — and in multimedia — the interaction of logic and reasoning within the realm of media. Within these contexts, the goal is the same — simulating intelligent agents that will react strategically to player behaviors and the environment. Improvements and advancements within this field will lead to increased veracity of simulations, enhanced learning within educational games, and more realistic and complicated gameplay. Additionally, advances in AI in games and media are worthy of study. This opens up the study to the area of multimedia — how are we using AI to shape the future of multimedia?