Miracle-dz/UniFAR-FTUs huggingface dataset:
Personalized Page Rank on Knowledge Graphs: Particle Filtering is all you need!

Graphs are everywhere. Personalized Page Rank (PPR) is a particularly important task to support search and exploration within such datasets. PPR computes the proximity between query nodes and other nodes in the graph. This is used, among others, for entity exploration, query expansion, and product recommendation. Graph databases are used for storing knowledge graphs. Unfortunately, the exact computation of PPR is computationally expensive. While different solutions have been proposed to compute PPR values with high precision, these are extremely complex to implement, and in some cases require heavy preprocessing. In this work, we sustain that a better approach exists: particle filtering. Particle filtering methods produce ranks with sufficient precision while exploiting what graph databases architectures are already optimized for: navigating local connections. We present the implementation of such an approach in a popular commercial database and show how this outperforms the already implemented functionality. With this, we aim to motivate future research to optimize and improve upon this research direction.
Sharing Computations for User-Defined Aggregate Functions

UDAFs (user-defined aggregate functions) are becoming a type of fundamental operators in advanced data analytics. The UDAF mechanism provided by most of the modern systems suffers, however, from at least two severe drawbacks: defining a UDAF requires hardcoding the routine that computes an aggregation, and the semantics of a UDAF is totally or partially unknown to the query processor, which hampers the optimization possibilities. This paper presents SUDAF (Sharing User-Defined Aggregate Functions), a declarative framework that allows users to write UDAFs as mathematical expressions and use them in SQL statements. SUDAF rewrites partial aggregates of UDAFs in users’ queries using built-in aggregate functions and supports efficient dynamic caching and reusing of partial aggregates. Our experiments show that rewriting UDAFs using built-in functions can significantly speed up queries with UDAFs, and the proposed sharing approach can yield up to two orders of magnitude improvement in query execution time.
Automated Management of Indexes for Dataflow Processing Engines in IaaS Clouds

Data structures like indexes are typically used to accelerate dataflow execution locating and accessing data more efficiently. The automated management of data structures has been a challenging problem, traditionally constrained by the time and storage required to build and maintain them. As cloud computing is becoming an attractive platform for the execution of dataflows with the usage of compute and storage resources being charged by cloud providers, monetary cost is becoming an equally important factor for the user to consider. In this work, we identify the opportunity of interleaving dataflow and index build operators in the execution schedule to utilize idle slots for the creation of indexes which may be beneficial for future dataflows. In that way, the cost of building indexes can be eliminated without impacting dataflow execution. We propose an online auto-tuning approach to assess the importance of indexes for the workload based on historical data taking into account the trade-off between the dataflow speed-up they offer and the monetary cost needed to maintain them. The results show that the proposed approach can dynamically adapt to the workload and significantly reduce the average execution time and cost spent per dataflow building and maintaining a proper set of indexes.
Declarative Languages for Big Streaming Data

The Big Data movement proposes data streaming systems to tame velocity and to enable reactive decision making. However, approaching such systems is still too complex due to the paradigm shift they require, i.e., moving from scalable batch processing to continuous data analysis and pattern detection. Recently, declarative Languages are playing a crucial role in fostering the adoption of Stream Processing solutions. In particular, several key players introduce SQL extensions for stream processing. These new languages are currently playing a central role in fostering the stream processing paradigm shift. In this tutorial, we give an overview of the various languages for declarative querying interfaces big streaming data. To this extent, we discuss how the different Big Stream Processing Engines (BigSPE) interpret, execute, and optimize continuous queries expressed with SQL-like languages such as KSQL , Flink-SQL , and Spark SQL . Finally, we present the open research challenges in the domain.
Chronos: The Swiss Army Knife for Database Evaluations

Systems evaluations are an important part of empirical research in computer science. Such evaluations encompass the systematic assessment of the run-time characteristics of systems based on one or several parameters. Considering all possible parameter settings is often a very tedious and time-consuming task with many manual activities, or at least the manual creation of evaluation scripts. Ideally, the thorough evaluation of a complete evaluation space can be fully automated. This includes the set-up of the evaluation, its execution, and the subsequent analysis of the results. In this paper, we introduce Chronos, a system for the automation of the entire evaluation workflow. While Chronos has originally been built for database systems evaluations, its generic approach also allows its usage in other domains. We show how Chronos can be deployed for a concrete database evaluation, the comparative performance analysis of different storage engines in MongoDB.
Fairness in Rankings and Recommenders

With the growing complexity of the available online information, search engines via rankings and recommender systems come to the rescue, providing suggestions to users about items of potential interest, from movies and products to news articles and even potential friends. Such results and suggestions aim at covering the user information needs and play an important role in guiding users’ decisions and in forming their opinions. However, the same technology, if not used responsibly, may lead to discrimination, amplify potential biases in the original data, restrict transparency and strengthen unfairness. For example, consider scenarios in which models based on biased data produce results that abet violence, decrease diversity, or have an adverse impact on economic policies. While the potential benefits of rankings and recommenders are well-accepted and understood, the importance of using such systems in a fair manner has only recently attracted attention. In this tutorial, we cover recent advancements and highlight future research directions in this increasingly relevant research area.
MALOS: A Movement-Aware Location Selection System

Facility placement has been receiving considerable research attention due to the proliferation of GPS equipped mobile devices. Establishing new facilities has a wide spectrum of applications including billboard placement, wildlife monitoring, supermar-ket/restaurant placement, etc. In all these applications, finding the best position to place a facility can lead to the optimal benefit given plenty of target users, which are associated with many historical position logs, e.g., user check-in data, wildlife monitor logs. In this work, we demonstrate a general system, namely MALOS, for answering the majority of facility placement problems given movement logs of a series of target users. Specifically, MALOS leverages on three academic works in the following scenarios. Find the best one/ k position/s to place one/ k new facilities in order that the overall benefit can be maximized; find one position to place an extra facility given that there are k existing facilities in different places, such that the overall benefit can be maximized. Notably, in MALOS we consider only the geographical issues that are common in all facility placement applications and choose not to take into account the specialized factors that vary across applications, such as billboard size, restaurant type, etc. We shall provide an opportunity for demonstration users to experience all kinds of these facility placement queries with a real-world historical movement logs and corresponding maps interface.
NoSQL Schema Evolution and Data Migration: State-of-the-Art and Opportunities

NoSQL database systems are very popular in agile software development. Naturally, agile deployment goes hand-in-hand with database schema evolution. The main aim of this tutorial is to present to the audience the current state-of-the-art in continuous NoSQL schema evolution and data migration: (1) We present case studies on schema evolution in NoSQL databases; (2) we survey existing approaches to schema management and schema inference, as implemented in popular NoSQL database products, and also as proposed in academic research; (3) we present approaches for extracting schema versions; (4) we analyze different methods for efficient NoSQL data migration; and (5) we give an outlook on further research opportunities.
Manually Detecting Errors for Data Cleaning Using Adaptive Crowdsourcing Strategies

Current work to detect data errors often uses (semi-)automatic solutions. In this paper, however, we argue that there are many real-world scenarios where users have to detect data errors completely manually , and that more attention should be devoted to this problem. We then study one instance of this problem in depth. Specifically, we focus on the problem of manually verifying the values of a target attribute , and shows that the current best solution in industry, which uses crowdsourcing, has significant limitations. We develop a new solution that addresses the above limitations. Our solution can find a much more accurate ranking of the data values in terms of their difficulties for crowdsourcing, can help domain experts debug this ranking, and can handle ambiguous values for which no golden answers exist. Importantly, our solution provides a unified framework that allows users to easily express and solve a broad range of optimization problems for crowdsourcing, to balance between cost and accuracy. Finally, we describe extensive experiments with three real-world data sets that demonstrate the utility and promise of our solution approach.
INR: A Programming Model for Developing APPs of Insect Intelligent Building

Insect Intelligent Building (I2B) is a novel platform of intelligent buildings. The outstanding feature of I2B is the decentralized network structure connected by smart nodes. I2B can employ APPs (applications) developed by various practitioners or programming fans to manage and control buildings. However, due to the unique parallel operation of I2B platform and the popularization of APP developers, there still exists no effective approach to supporting I2B APP development. To deal with the challenges and provide meaningful guidance for describing and developing I2B APP and motivating the prospective programming language design, we propose INR, a programming model for I2B APP development. Three submodels in INR, namely, Individual, Neighborhood, and Region, are defined and implemented, respectively, for describing different task requirements. Moreover, new mechanisms of Tag-based programming and Clustering operation are established to support the plug-and-play and parallel abilities of APPs in I2B. Finally, we apply the programming model into an application case to illustrate the developing pattern of the I2B APP and verify the effectiveness of our approach.
Scaling Up Record-level Matching Rules

Record-level matching rules are chains of similarity join predicates on multiple attributes employed to join records that refer to the same real-world object when an explicit foreign key is not available on the data sets at hand. They are widely employed from data scientists and practitioners that work with data lakes, open data, and data in the wild. We present RulER , the first tool that allows to efficiently execute record-level matching rules on parallel and distributed systems—we developed that on top of Apache Spark to leverage on its vast ecosystem of libraries and wide adoption. In this demo, we show how RulER can be easily employed for data preparation tasks (i.e., to join data sets to be consumed by data analytic tasks) and to support the user in debugging record-level matching rules. Finally, we demonstrate how our execution strategy of the record-level matching rules—introduced by RulER —is up to 3 times faster than the naïve concatenation of similarity join predicates.
Inventory Reduction via Maximal Coverage in E-Commerce

Many e-commerce platforms serve as an intermediary between companies and consumers, receiving a commission per purchase. To increase sales, these platforms tend to offer as many items as possible. However, in many situations a reduced subset of the items should be offered for sale, e.g., when opening an express delivery branch, starting operations in a new region, or disposing of redundant items to improve data quality and decrease maintenance costs. In all these cases it is imperative to select a reduced inventory which maximally covers consumer needs. A naïve, yet popular, solution is to focus on the top selling items. This however ignores the hidden relations between items, and in particular the tendency of shoppers to buy, in the absence of an item they are looking for, a satisfying alternative. In this paper we introduce the Preference Cover problem , and investigate its application to practical inventory reduction. Given a large set of items, a bound on the number of items that can be retained and consumer preferences in terms of items popularity and suitability as alternatives, the goal is to select a reduced inventory which maximizes the likelihood of a purchase. We first model the problem via a dedicated weighted directed graph which captures the relevant information, then study two problem variants, which differ in their interpretation of the probabilistic dependencies be-tween consumer preferences. We prove both variants are NP-hard, and characterize their approximation hardness. Since in the practical application the overall number of items and the bound on the reduced item set are very large - in the order of magnitude of millions - we propose a highly parallelizable and scalable algo-rithm along with approximation guarantees. Finally, we present an end-to-end solution that fits the real-world e-commerce application, and provide an extensive set of experiments demonstrating the efficiency and effectiveness of our solution.
Gallery: A Machine Learning Model Management System at Uber

Machine learning is critical to the success of many products across application domains. At Uber, we have a variety of machine learning applications including matching, pricing, recommendation, and personalization. As a result, we have a large number of machine learning models to manage in production. Generally, building machine learning models is an iterative process and machine learning models span across a set of stages of a lifecycle. In this paper, we describe Gallery, a machine learning model lifecycle management system to save and serve models and metrics and automatically orchestrate the � ow of models across di � erent stages in the lifecycle. We then use the Uber Marketplace Forecasting and Simulation platforms as examples to show how Uber uses Gallery in production and the bene � ts we get by using Gallery.
SlideSide: A fast Incremental Stream Processing Algorithm for Multiple Queries

Aggregate window computations lie at the core of online analytics in both academic and industrial applications. To efficiently compute sliding windows, the state-of-the-art algorithms utilize incremental processing that avoids the recomputation of window results from scratch. In this paper, we propose a novel algorithm, called SlideSide, that extends TwoStacks for multiple concurrent aggregate queries over the same data stream. Our approach uses different yet similar processing schemes for invertible and non-invertible functions and exhibits up to 2 × better through-put compared to the state-of-the-art incremental techniques in a multi-query environment.
Automatic Canonical Utterance Generation for Task-Oriented Bots from API Specifications

With the mind-blowing development of REST (REpresentational State Transfer) APIs (Application Programming Interfaces), many applications have been designed to harness their potential. As such, bots have recently become interesting interfaces to connect humans to APIs. Supervised approaches for building bots rely upon a large set of user utterances paired with API methods. Collecting such pairs is typically done by obtaining initial utterances for a given API method and paraphrasing them to obtain new variations. However, existing approaches for generating initial utterances (e.g., creating sentence templates) do not scale and are domain-specific, making bots expensive to maintain. The automatic generation of initial utterances can be considered as a supervised translation task in which an API method is translated into an utterance. However, the key challenge is the lack of training data for training domain-independent models. In this paper, we propose API2CAN , a dataset containing 14,370 pairs of API methods and utterances. The dataset is built by processing a large number of public APIs. However, deep-learning-based approaches such as sequence-to-sequence models require larger sets of training samples (ideally millions of samples). To mitigate the absence of such large datasets, we formalize and define resources in REST APIs, and we propose a delexicalization technique (by converting an API method and initial utterances to tagged sequences of resources) to let deep-learning-based approaches learn from such datasets.
On-device Chatbot System using SuperChat Method on Raspberry Pi and CNN Domain Specific Accelerator

Chatbot is a popular interactive entertainment device requires semantic understanding and natural language processing of input inquiries and appropriate individualized responses. Currently, most chatbot services are provided with connection to cloud due to the limitation of computation power on edge devices, which brings in the privacy and latency concerns. However, the recent research on SuperChat method shows that the chit-chat tasks can be solved using two-dimensional CNN models. In addition, low-power CNN Domain Speciﬁc Accelera-tors are in wide availability since the past two or three years. In this paper, we implement SuperChat method on a Raspberry Pi 3.0 connected through USB to a low-power CNN accelerator chip, which is loaded with the quantized weights two-dimensional CNN model. The resulting system can reach convincing accuracy with high power, memory eﬃciency, and very low power consumption.
Explaining Missing Query Results in Natural Language

We propose in this paper a novel approach for explaining query non-answers in Natural Language within the context of Natural Language Interfaces to Databases (NLIDBs). Such interfaces allow non-expert users to pose queries over an underlying database; our goal is to further allow users to ask why some results that they have expected to see, are missing from the output. In a nutshell, our approach is to “marry" NLIDBs with an existing model for explaining missing query results by pinpointing the last query operator that is “responsible" for the missing result. We observe that one can often trace the parts of the original NL question that correspond to these operators. This paves the way for intuitive explanations of the non-answers, that are based on highlighting the relevant parts of the question. Our architecture is generic and is not coupled with a specific NLIDB, and our solution yields clear explanations in interactive speed.
Zooming Out on an Evolving Graph

An evolving graph maintains the history of changes of graph topology and attribute values over time. Such a graph has a specific temporal and structural resolution. It is often useful to modify this resolution during analysis, for example, to consider communities rather than individual nodes, or to quantify changes at the level of days rather than hours. We propose attribute-based zoom and temporal window-based zoom — two operators that support exploratory analysis of an evolving graph at different levels of resolution. We develop several alternative physical representations of an evolving property graph — a temporal generalization of a property graph — and detail how to implement the proposed zoom operators using dataflow operations. These different physical representations allow us to explore the trade-offs in temporal and structural locality with respect to the performance of the zoom operators. We implement the operators in Apache Spark, evaluate them on real evolving graph datasets, and demonstrate scalability to billion-edge graphs.
On the Unlinkability of Fingerprint Shell

To prevent the leakage of original biometric information of a user, it may be transformed into a cancelable form. A cancelable biometric transformation should satisfy four requirements: unlinkability, revocability, noninvertibility, and performance. In 2014, Moujahdi et al. proposed a new cancelable fingerprint transformation called fingerprint shell, which was also later discussed by Ali et al. In this paper, we show that all of the shell fingerprint schemes presented by Moujahdi et al. and Ali et al. do not satisfy the condition of unlinkability.
A SURVEY ON CHALLENGES IN SENTIMENT ANALYSIS

 Abstract — The rapid growth of data in the internet increase the mining of unstructured data to get valuable results. Nowadays one of the hottest research areas in computer science is sentiment analysis. Sentiment analysis is a type of natural language processing for tracking the mood of the public about a particular product or topic. In digital age, online reviews about the products are mostly used for sentiment analysis. And it became a hotspot topic in web mining. Text classification is a type of multiclass classification where as basic idea of sentiment analysis is to classify the polarity of the text in terms of positive, negative and neutral. The main challenge in the opinion mining is to identify the sentiment expressed by the text. This paper describes some of the considerable challenges in sentiment analysis and the techniques use to analyze.