Reviewerly/exHarmony huggingface dataset:
Solar Power Forecasting Using Deep Learning Approach

Renewable energy resources, especially solar energy is playing a larger role in hybrid generation. Solar power has a significant impact on future and exists a number of technological, environmental and political challenges scaled on energy plants. Solar power forecasting can avoid many of the balancing issues, if accurate forecasts of solar output are available. Challenges facing by solar modules are its operational and maintenance costs. Deep learning techniques, particularly those based on long short-term memory (LSTM) and convolutional neural network (CNN), are used in this paper. Solar power forecasting in educational buildings has become extremely challenging. In this study, the solar power generation of an educational building, i.e., Visvesvaraya national institute of technology (VNIT), Nagpur, has been taken. The metrics is evaluated by comparing RMSE error difference between LSTM and CNN is 0.03% and LR is 24.9% and similarly for other matrices. Among all models, LSTM performs best and makes accurate predictions for forecasting solar power.
On Selection of Solar Position-Dependent Regressor Set for Variability Modeling of Nature-Inspired Time Series

Variability in the dataset of a variable refers to the periodically repeating component. Since variability comprises a specific pattern (skewed and/or multimodality) that is repeating; therefore, the pattern can be predictable. Modeling this predictable component has drawn enormous research interest in many engineering fields. When the variable of interest is nature-inspired, its variability modeling requires a regression-based mathematical framework where regressor selection is challenging. Any random regressor selection is generally not a choice as it decreases the model’s accuracy. On this note, the selection of relevant natural driving factors of the variable is essential to determine. This paper comprehensively addresses various implementation steps for modeling nature-inspired time series by considering an ambient temperature time series.
A Decision Framework for Assessing and Improving the Barriers of Blockchain Technology Adoption

Blockchain is regarded as a mainstream technology by corporate leaders, who believe it will drive a new generation of business models. The trends of digital innovation have accelerated corporate blockchain technology adoptions, but when it comes to its applications, numerous barriers still exist that must be carefully addressed. In contrast to past decades, data gathering with the help of information technology has switched from being an expensive to an inexpensive process. It brings users sufficient messages, but also incurs the information overload problem. To combat this, a fusion framework integrated FRST-PSO and fuzzy DEMATEL is introduced herein. It assists users in identifying essential information and depicting the opaque relationships among criteria. The findings indicate that improvement priority, which runs in the order of regulatory environment, blockchain development talent, system integration, and function and reliability based on the magnitude of the impact, serves as a reference for the blockchain technology adoption to facilitate/solidify a firm's competitive edges.
Demonstrating ADOPT: Adaptively Optimizing Attribute Orders for Worst-Case Optimal Joins via Reinforcement Learning

Performance of worst-case optimal join algorithms depends on the order in which the join attributes are processed. It is challenging to identify suitable orders prior to query execution due to the huge search space of possible orders and unreliable execution cost estimates in case of data skew or data correlation. We demonstrate ADOPT, a novel query engine that integrates adaptive query processing with a worst-case optimal join algorithm. ADOPT divides query execution into episodes, during which different attribute orders are invoked. With runtime feedback on performance of different attribute orders, ADOPT rapidly approaches near-optimal orders. Moreover, ADOPT uses a unique data structure which keeps track of the processed input data to prevent redundant work across different episodes. It selects attribute orders to try via reinforcement learning, balancing the need for exploring new orders with the desire to exploit promising orders. In experiments, ADOPT outperforms baselines, including commercial and open-source systems utilizing worst-case optimal join algorithms, particularly for complex queries that are difficult to optimize.
On the Cusp: Computing Thrills and Perils and Professional Awakening

Over the past eight decades, computer science has advanced as a field, and the computing profession has matured by establishing professional codes of conduct, fostering best practices, and establishing industry standards to support the proliferation of technologies and services. Research and applications of digital computation continue to change all aspects of human endeavor through new waves of innovation. While it is clear that different research advances fuel innovation, the ways they come together to make an impact vary. In contrast to highly regulated sectors such as pharma, medicine and law, the process of transforming research into widely deployed technologies is not regulated. We reflect on collective practices, from discovery by scientists and engineers to market delivery by entrepreneurs, industry leaders, and practitioners. We consider ecosystem changes that are required to sustain the transformational effects of new technologies and enable new practices to take root. Every such transformation ruptures in the existing socio-technical fabric and requires a concerted effort to remedy this through effective policies and regulations. Computing experts are involved in all phases and must match the transformational power of their innovation with the highest standard of professional conduct. We highlight the principles of responsible innovation and discuss three waves of digital innovation. We use wide and uncontrolled generative AI deployments to illustrate risks from the implosion of digital media due to contamination of digital records, removal of human agency, and risk to an individual's personhood.
SHEVA: A Visual Analytics System for Statistical Hypothesis Exploration

We demonstrate SHEVA, a System for Hypothesis Exploration with Visual Analytics. SHEVA adopts an Exploratory Data Analysis (EDA) approach to discovering statistically-sound insights from large datasets. The system addresses three longstanding challenges in Multiple Hypothesis Testing: (i) the likelihood of rejecting the null hypothesis by chance, (ii) the pitfall of not being representative of the input data, and (iii) the ability to navigate among many data regions while preserving the user's train of thought. To address (i) &amp; (ii), SHEVA implements significance adjustment methods that account for data-informed properties such as coverage and novelty. To address (iii), SHEVA proposes to guide users by recommending one-sample and two-sample hypotheses in a stepwise fashion following a data hierarchy. Users may choose from a collection of pre-trained hypothesis exploration policies and let SHEVA guide them through the most significant hypotheses in the data, or intervene to override suggested hypotheses. Furthermore, SHEVA relies on data-to-visual element mappings to convey hypothesis testing results in an interpretable fashion, and allows hypothesis pipelines to be stored and retrieved later to be tested on new datasets.
Generations of Knowledge Graphs: The Crazy Ideas and the Business Impact

Knowledge Graphs (KGs) have been used to support a wide range of applications, from web search to personal assistant. In this paper, we describe three generations of knowledge graphs: entity-based KGs , which have been supporting general search and question answering ( e.g. , at Google and Bing); text-rich KGs , which have been supporting search and recommendations for products, bio-informatics, etc. ( e.g. , at Amazon and Alibaba); and the emerging integration of KGs and LLMs, which we call dual neural KGs. We describe the characteristics of each generation of KGs, the crazy ideas behind the scenes in constructing such KGs, and the techniques developed over time to enable industry impact. In addition, we use KGs as examples to demonstrate a recipe to evolve research ideas from innovations to production practice, and then to the next level of innovations, to advance both science and business.
RESCU-SQL: Oblivious Querying for the Zero Trust Cloud

Cloud service providers offer robust infrastructure for rent to organizations of all kinds. High stakes applications, such as the ones in defense and healthcare, are turning to the public cloud for a cost-effective, geographically distributed, always available solution to their hosting needs. Many such users are unwilling or unable to delegate their data to this third-party infrastructure. In this demonstration, we introduce RESCU-SQL, a zero-trust platform for resilient and secure SQL querying outsourced to one or more cloud service providers. RESCU-SQL users can query their DBMS using cloud infrastructure alone without revealing their private records to anyone. It does so by executing the query over secure multiparty computation. We call this system zero trust because it can tolerate any number of malicious servers provided one of them remains honest. Our demo will offer an interactive dashboard with which attendees can observe the performance of RESCU-SQL deployed on several in-cloud nodes for the TPC-H benchmark. Attendees can select a computing party and inject messages from it to explore how quickly it detects and reacts to a malicious party. This is the first SQL system to support all-but-one maliciously secure querying over a semi-honest coordinator for efficiency.
EQUI-VOCAL Demonstration: Synthesizing Video Queries from User Interactions

We demonstrate EQUI-VOCAL, a system that synthesizes compositional queries over videos from user feedback. EQUI-VOCAL enables users to query a video database for complex events by providing a few positive and negative examples of what they are looking for and labeling a small number of additional system-selected examples. Using those user inputs, EQUI-VOCAL synthesizes declarative queries that can then retrieve additional instances of the desired events. The demonstration makes two contributions: it introduces EQUI-VOCAL's graphical user interface and enables conference attendees to experiment with EQUI-VOCAL on a variety of queries. Both enable users to gain a better understanding of EQUI-VOCAL's query synthesis approach and to explore the impact of hyperparameters and label noise on system performance.
ChainDash: An Ad-Hoc Blockchain Data Analytics System

The emergence of digital asset applications, driven by Web 3.0 and powered by blockchain technology, has led to a growing demand for blockchain-specific graph analytics to unearth the insights. However, current blockchain data analytics systems are unable to perform efficient ad-hoc graph analytics over both live and past time windows due to their inefficient data synchronization and slow graph snapshots retrieval capability. To address these issues, we propose ChainDash, a blockchain data analytics system that dedicates a highly-parallelized data synchronization component and a retrieval-optimized temporal graph store. By leveraging these techniques, ChainDash supports efficient ad-hoc graph analytics of smart contract activities over arbitrary time windows. In the demonstration, we showcase the interactive visualization interfaces of ChainDash, where attendees will execute customized queries for ad-hoc graph analytics of blockchain data.
Demonstration of SPARQL <sup> <i>ML</i> </sup> : An Interfacing Language for Supporting Graph Machine Learning for RDF Graphs

This demo paper presents KGNet, a graph machine learning-enabled RDF engine. KGNet integrates graph machine learning (GML) models with existing RDF engines as query operators to support node classification and link prediction tasks. For easy integration, KGNet extends the SPARQL language with user-defined predicates to support the GML operators. We refer to this extension as SPARQL ML query. Our SPARQL ML query optimizer is in charge of optimizing the selection of the near-optimal GML models. The development of KGNet poses research opportunities in various areas spanning KG management. In the paper, we demonstrate the ease of integration between the RDF engines and GML models through the SPARQL ML inference query language. We present several real use cases of different GML tasks on real KGs. Using KGNet, users do not need to learn a new scripting language or have a deep understanding of GML methods. The audience will experience KGNet with different KGs and GML models, as shown in our demo video and Colab notebook.
ADOps: An Anomaly Detection Pipeline in Structured Logs

Anomaly detection has been extensively implemented in industry. The reality is that an application may have numerous scenarios where anomalies need to be monitored. However, the complete process of anomaly detection will take much time, including data acquisition, data processing, model training, and model deployment. In particular, some simple scenarios do not require building complex anomaly detection models. This results in a waste of resources. To solve these problems, we build an anomaly detection pipeline(ADOps) to modularize each step. For simple anomaly detection scenarios, no programming is required and new anomaly detection tasks can be created by simply modifying the configuration file. In addition, it can also improve the development efficiency of complex anomaly detection models. We show how users create anomaly detection tasks on the anomaly detection pipeline and how engineers use it to develop anomaly detection models.
DHive: Query Execution Performance Analysis via Dataflow in Apache Hive

Nowadays, Apache Hive has been widely used for large-scale data analysis applications in many organizations. Various visual analytical tools are developed to help Hive users quickly analyze the query execution process and identify the performance bottleneck of executed queries. However, existing tools mostly focus on showing the time usage of query sub-components (jobs and operators) but fail to provide enough evidence to analyze the root reasons for the slow execution progress. To tackle this problem, we develop a visual analytical system DHive to visualize and analyze the query execution progress via dataflow analysis. DHive shows the dataflow during query execution at multiple levels: query level, job level and task level, which enable users to identify the key jobs/tasks and explain their time usage by linking them to the auxiliary information such as the system configuration and hardware status. We demonstrate the effectiveness of DHive by two cases in a production cluster. DHive is open-source at https://github.com/DBGroup-SUSTech/DHive.git.
KG-Roar: Interactive Datalog-Based Reasoning on Virtual Knowledge Graphs

Logic-based Knowledge Graphs (KGs) are gaining momentum in academia and industry thanks to the rise of expressive and efficient languages for Knowledge Representation and Reasoning (KRR). These languages accurately express business rules, through which valuable new knowledge is derived. A versatile and scalable backend reasoner, like Vadalog, a state-of-the-art system for logic-based KGs---based on an extension of Datalog---executes the reasoning. In this demo, we present KG-Roar, a web-based interactive development and navigation environment for logical KGs. The system lets the user augment an input graph database with intensional definitions of new nodes and edges and turn it into a KG, via the metaphor of reasoning widgets---user-defined or off-the-shelf code snippets that capture business definitions in the Vadalog language. Then, the user can seamlessly browse the original and the derived nodes and edges within a "Virtual Knowledge Graph", which is reasoned upon and generated interactively at runtime, thanks to the scalability and responsiveness of Vadalog. KG-Roar is domain-independent but domain aware, as exploration controls are contextually generated based on the intensional definitions. We walk the audience through KG-Roar showcasing the construction of certain business definitions and putting it into action on a real-world financial KG, from our work with the Bank of Italy.
Demonstration of OpenDBML, a Framework for Democratizing In-Database Machine Learning

Machine learning over relational data has been used in several applications. The traditional approach of joining relations first and then training a model on the joined table is time-consuming and requires a significant amount of memory. Recent research has focused on in-database machine learning (in-DB ML) to address this issue; these methods train the models over relations without joining, resulting in a more efficient process. However, such systems have ad-hoc user interfaces and specific data formats, making them challenging to use. To address this problem, this paper presents OpenDBML, a framework for democratizing in-DB ML. OpenDBML offers a Python interface for multiple in-DB ML systems, a set of commonly used datasets, and the ability to add new datasets and in-DB ML systems via both Python and web interfaces. The paper also presents comprehensive demonstration scenarios to illustrate how to use OpenDBML effectively.
PSFQ: A Blockchain-Based Privacy-Preserving and Verifiable Student Feedback Questionnaire Platform

Recently, more and more higher education institutions have been using student feedback questionnaires (SFQ) to evaluate teaching. However, existing SFQ systems have two shortcomings. The first is that the respondent of an SFQ is not anonymous. The second is that the statistical report of SFQs can be manipulated. To tackle these two shortcomings, we develop a novel SFQ system, namely PSFQ. In PSFQ, the respondent of an SFQ is mixed with multiple users by a ring signature. PSFQ uses an advanced ring signature approach to minimize the size of a ring signature when anonymity satisfies the requirements. Thus, the first shortcoming has been overcome. Moreover, all answers are encrypted by homomorphic encryption and stored on the blockchain, enabling users to verify the correctness of the statistical reports. Our demonstration will showcase how PSFQ provides confidential SFQ responses while ensuring the correctness of statistical reports.
VisualNeo: Bridging the Gap between Visual Query Interfaces and Graph Query Engines

Visual Graph Query Interfaces (VQIs) empower non-programmers to query graph data by constructing visual queries intuitively. Devising efficient technologies in Graph Query Engines (GQEs) for interactive search and exploration has also been studied for years. However, these two vibrant scientific fields are traditionally independent of each other, causing a vast barrier for users who wish to explore the full-stack operations of graph querying. In this demonstration, we propose a novel VQI system built upon Neo4j called VisualNeo that facilities an efficient subgraph query in large graph databases. VisualNeo inherits several advanced features from recent advanced VQIs, which include the data-driven gui design and canned pattern generation. Additionally, it embodies a database manager module in order that users can connect to generic Neo4j databases. It performs query processing through the Neo4j driver and provides an aesthetic query result exploration.
Explaining Differentially Private Query Results with DPXPlain

Employing Differential Privacy (DP), the state-of-the-art privacy standard, to answer aggregate database queries poses new challenges for users to understand the trends and anomalies observed in the query results: Is the unexpected answer due to the data itself, or is it due to the extra noise that must be added to preserve DP? We propose to demonstrate DPXPlain, the first system for explaining group-by aggregate query answers with DP. DPXPlain allows users to compare values of two groups and receive a validity check, and further provides an explanation table with an interactive visualization, containing the approximately 'top-k' explanation predicates along with their relative influences and ranks in the form of confidence intervals, while guaranteeing DP in all steps.
Lynx: A Graph Query Framework for Multiple Heterogeneous Data Sources

Graph model are increasingly popular among modern applications for its ability to model complex relationships between entities. Users tend to query the data as a graph with graph operations (e.g., graph navigation and exploration). However, a large fraction of the data resides in relational databases or other storage systems. Challenges arise in uniformly querying multiple heterogeneous data sources as a graph. Traditional solutions are limited by time-consuming data integration, expensive development effort, and incomplete query requirements. Thus, we developed Lynx, a general graph query framework, to simplify querying graph data by converting complex statements into basic graph operations. Instead of connecting directly to the data sources, Lynx retrieves data through user-implemented interfaces for those graph operations. We demonstrate Lynx's capabilities through real-world scenarios, showcasing Lynx's ability to process graph queries on multiple heterogeneous data sources and also to be used as a generic graph query engine development framework.
PAINE Demo: Optimizing Video Selection Queries with Commonsense Knowledge

Because video is becoming more popular and constitutes a major part of data collection, we have the need to process video selection queries --- selecting videos that contain target objects. However, a naïve scan of a video corpus without optimization would be extremely inefficient due to applying complex detectors to irrelevant videos. This demo presents Paine; a video query system that employs a novel index mechanism to optimize video selection queries via commonsense knowledge. Paine samples video frames to build an inexpensive lossy index, then leverages probabilistic models based on existing commonsense knowledge sources to capture the semantic-level correlation among video frames, thereby allowing Paine to predict the content of unindexed video. These models can predict which videos are likely to satisfy selection predicates so as to avoid Paine from processing irrelevant videos. We will demonstrate a system prototype of Paine for accelerating the processing of video selection queries, allowing VLDB'23 participants to use the Paine interface to run queries. Users can compare Paine with the baseline, the SCAN method.
KGNav: A Knowledge Graph Navigational Visual Query System

Visual query is a vital technique for comprehending and analyzing knowledge graphs, which provides an effective method to lower the barrier of querying knowledge graphs for non-professional users. Nevertheless, visual query techniques for knowledge graphs and ontologies that have emerged in recent years cannot bridge the gap between global information provided by the knowledge graph schema and underlying data of knowledge graph. Thus it cannot fully exploit the global information to navigate users for querying knowledge graphs. This demonstration showcases KGNav, a Knowledge Graph Navigational visual query system. KGNav (1) redefines the minimal unit of operation to abstract the conceptual hierarchy, i.e., Knowledge Graph Schema, in the domain from the original knowledge graph in an offline semi-automatic way through the equivalence relations between these units; it also (2) provides a series of operators and an interactive GUI to capture user query intentions, guiding users to explore the Knowledge Graph Schema to achieve in-depth analysis of knowledge graphs. We will demonstrate the capability of KGNav in reducing tedious queries, enabling users to swiftly grasp the structure of the knowledge graph, and performing queries through several fundamental scenarios.