Knowledge bases (KBs), which store high-quality information, are crucial for many applications, such as enhancing search results and serving as external sources for data cleaning. Not surprisingly, there exist outdated facts in most KBs due to the rapid change of information. Naturally, it is important to keep KBs up-to-date. Traditional wisdom has investigated the problem of using reference data (such as new facts extracted from the news) to detect outdated facts in KBs. However, existing approaches can only cover a small percentage of facts in KBs. In this paper, we proposeHOFD, a novel human-in-the-loop approach for outdated fact detection in KBs.HOFDtrains a binary classifier using features such as historical update frequency and update time of a fact to compute the likelihood of a fact in a KB to be outdated. Then,HOFDinteracts with humans to verify whether a fact with high likelihood is indeed outdated. In addition,HOFDalso uses logical rules to detect more outdated facts based on human feedback. The outdated facts detected by the logical rules will also be fed back to train the ML model further fordata augmentation. Extensive experiments on real-world KBs, such as Yago and DBpedia, show the effectiveness of our solution.

As an important part of database optimization, index selection problem remains a hot topic. Existing methods tend to use the cost estimated by the DBMS optimizer to measure the benefit of an index. However, due to the limitations of the cost estimation model in database management system (DBMS), these methods may not find the optimal index configuration. To address this problem, we present SmartIndex, an index advisor for relational database with learned cost estimator. We first design a graph convolutional network (GCN) based cost estimation model to predict a query's execution time on certain indexes. After that, we use a greedy method for index selection under certain constraints including number of indexes and storage cost of indexes, which can find better solutions for a given workload.

Most real-world datasets contain label noise, which can negatively affect downstream ML models trained on them. To deal with this problem, one can clean the mislabeled data before training, which is not only time-consuming and expensive but also requires domain expertise. Another approach is to use a noise-robust ML training algorithm. However, existing methods have some prerequisites that may not be practical in many applications (e.g., they are tied to specific downstream model architecture or they are applicable to specific noise distributions). In this paper, we propose a model-agnostic approach for learning with noisy labels of arbitrary distributions. In particular, our approach can work with any gradient descent optimization based machine learning model and deal with any label noise distribution. We achieve them by proposing two theoretically grounded noise-robust loss functions (for different noise distributions), and we are able to automatically decide which loss function to use based on a novel noise setting detection module. We directly learn the required hyper-parameters in the loss functions via meta-learning technique to minimize the loss on a given small clean validation set, and propose several strategies to improve the efficiency of training. Experiments on multiple datasets with both real-world and injected label noise show that our method performs better than state-of-the-art approaches.

Prediction of particle radiative heat transfer flux is an important task in the large discrete granular systems, such as pebble bed in power plants and industrial fluidized beds. For particle motion and packing, discrete element method (DEM) now is widely accepted as the excellent Lagrangian approach. For thermal radiation, traditional methods focus on calculating the obstructed view factor directly by numerical algorithms. The major challenge for the simulation is that the method is proven to be time-consuming and not feasible to be applied in the practical cases. In this work, we propose an analytical model to calculate macroscopic effective conductivity from particle packing structures Then, we develop a deep neural network (DNN) model used as a predictor of the complex view factor function. The DNN model is trained by a large dataset and the computational speed is greatly improved with good accuracy. It is feasible to perform real-time simulation with DNN model for radiative heat transfer in large pebble bed. The trained model also can be coupled with DEM and used to analyze efficiently the directional radiative conductivity, anisotropic factor and wall effect of the particle thermal radiation.

Knowledge bases (KBs), which store high-quality information, are crucial for many applications, such as enhancing search results and serving as external sources for data cleaning. Not surprisingly, there exist outdated facts in most KBs due to the rapid change of information. Naturally, it is important to keep KBs up-to-date. Traditional wisdom has investigated the problem of using reference data (such as new facts extracted from the news) to detect outdated facts in KBs. However, existing approaches can only cover a small percentage of facts in KBs. In this paper, we propose a novel human-in-the-loop approach for outdated fact detection in KBs. It trains a binary classifier using features such as historical update frequency and existence time of a fact to compute the likelihood of a fact in a KB to be outdated. Then, it interacts with humans to verify whether a fact with high likelihood is indeed outdated. In addition, it also uses logical rules to detect more outdated facts based on human feedback. The outdated facts detected by the logical rules will also be fed back to train the ML model further for data augmentation. Extensive experiments on real-world KBs, such as Yago and DBpedia, show the effectiveness of our solution.

A data summarization for the large table can be of great help, which provides a concise and informative overview and assists the user to quickly figure out the subject of the data. However, a high quality summarization needs to have two desirable properties: presenting notable entities and achieving broad domain coverage. In this demonstration, we propose a summarizer system called PocketView that is able to create a data summarization through a pocket view of the table. The attendees will experience the following features of our system:(1) time-sensitive notability evaluation - PocketView can automatically identify notable entities according to their significance and popularity in user-defined time period; (2) broad-coverage pocket view - Our system will provide a pocket view for the table without losing any domain, which is much simpler and clearer for attendees to figure out the subject compared with the original table.

Entity categorization - the process of grouping entities into categories for some specific purpose - is an important problem with a great many applications, such as Google Scholar and Amazon products. Unfortunately, in practice, many entities are mis-categorized. In this paper, we study the problem of discovering mis-categorized entities from a given group of entities. This problem is inherently hard: all entities within the same group have been "well" categorized by state-of-the-art solutions. Apparently, it is nontrivial to differentiate them. We propose a novel rule-based framework to solve this problem. It first uses positive rules to compute disjoint partitions of entities, where the partition with the largest size is taken as the correctly categorized partition, namely the pivot partition. It then uses negative rules to identify mis-categorized entities in other partitions that are dissimilar to the entities in the pivot partition. We describe optimizations on applying these rules, and discuss how to generate positive/negative rules. Extensive experimental results on two real-world datasets show the effectiveness of our solution.

Entity categorization - the process of grouping entities into categories for some specific purpose - is an important problem with a great many applications, such as Google Scholar and Amazon products. Unfortunately, many real-world categories contain mis-categorized entities, such as publications in one's Google Scholar page that are published by the others. We have proposed a general framework for a new research problem - discovering mis-categorized entities. In this demonstration, we have developed a Google Chrome extension, namely GSCleaner, as one important application of our studied problem. The attendees will have the opportunity to experience the following features: (1) mis-categorized entity discovery - The attendee can check mis-categorized entities on anyone's Google Scholar page; and (2) Cleaning onsite - Any attendee can login and clean his Google Scholar page using GSCleaner.We describe our novel rule-based framework to discover mis-categorized entities. We also propose effective optimization techniques to apply the rules. Some empirical results show the effectiveness of GSCleaner on discovering mis-categorized entities.

Integrity constraint (IC) based data repairing is typically an iterative process consisting of two parts: detecting and grouping errors that violate given ICs, and modifying values inside each group such that the modified database satisfies those ICs. However, most existing automatic solutions treat the process of detecting and grouping errors straightforwardly (e.g., violations of functional dependencies using string equality), while putting more attention on heuristics of modifying values within each group. In this paper, we propose a revised semantics of violations and data consistency w.r.t. a set of ICs. The revised semantics relies on string similarities, in contrast to traditional methods that use syntactic error detection using string equality. Along with the revised semantics, we also propose a new cost model to quantify the cost of data repairing by considering distances between strings. We show that the revised semantics provides a significant change for better detecting and grouping errors, which in turn improves both precision and recall of the following data repairing step. We prove that finding minimumcost repairs in the new model is NP-hard, even for a single FD. We devise efficient algorithms to find approximate repairs.

We study the data cleaning problem of detecting and repairing wrong relational data, as well as marking correct data, using well curated knowledge bases (KBs). We propose detective rules (DRs), a new type of data cleaning rules that can make actionable decisions on relational data, by building connections between a relation and a KB. The main invention is that, a DR simultaneously models two opposite semantics of a relation using types and relationships in a KB: the positive semantics that explains how attribute values are linked to each other in correct tuples, and the negative semantics that indicates how wrong attribute values are connected to other correct attribute values within the same tuples. Naturally, a DR can mark correct values in a tuple if it matches the positive semantics. Meanwhile, a DR can detect/repair an error if it matches the negative semantics. We study fundamental problems associated with DRs, e.g., rule generation and rule consistency. We present efficient algorithms to apply DRs to clean a relation, based on rule order selection and inverted indexes. Extensive experiments, using both real-world and synthetic datasets, verify the effectiveness and efficiency of applying DRs in practice.

Integrity constraint based data repairing is an iterative process consisting of two parts: detect and group errors that violate given integrity constraints (ICs); and modify values inside each group such that the modified database satisfies those ICs. However, most existing automatic solutions treat the process of detecting and grouping errors straightforwardly (e.g., violations of functional dependencies using string equality), while putting more attention on heuristics of modifying values within each group. In this paper, we propose a revised semantics of violations and data consistency w.r.t. a set of ICs. The revised semantics relies on string similarities, in contrast to traditional methods that use syntactic error detection using string equality. Along with the revised semantics, we also propose a new cost model to quantify the cost of data repair by considering distances between strings. We show that the revised semantics provides a significant change for better detecting and grouping errors, which in turn improves both precision and recall of the following data repairing step. We prove that finding minimum-cost repairs in the new model is NP-hard, even for a single FD. We devise efficient algorithms to find approximate repairs. In addition, we develop indices and optimization techniques to improve the efficiency. Experiments show that our approach significantly outperforms existing automatic repair algorithms in both precision and recall.

Emerging vehicular trajectory data have opened up opportunities to benefit many real-world applications, e.g., frequent trajectory based navigation systems, road planning, car pooling, etc. The similarity join is a key operation to enable such applications, which finds similar trajectory pairs from two large collections of trajectories. Existing similarity metrics on trajectories rely on aligning sampling points of two trajectories. However, due to different sampling rates or different vehicular speeds, the sample points in similar trajectories may not be aligned. To address this problem, we propose a new bi-directional mapping similarity (BDS), which allows a sample point of a trajectory to align to the closest location (which may not be a sample point) on the other trajectory, and vice versa. Since it is expensive to enumerate every two trajectories and compute their similarity, we propose Strain-Join, a signature-based trajectory similarity join framework. Strain-Join first generates signatures for each trajectory such that if two trajectories do not share common signatures, they cannot be similar. In order to utilize this property to prune dissimilar pairs, we devise several techniques to generate high-quality signatures and propose an efficient filtering algorithm to prune dissimilar pairs. For the pairs not pruned by the filtering algorithm, we propose effective verification algorithms to verify whether they are similar. Experimental results on real datasets show that our algorithm outperforms state-of-the-art techniques in terms of both effectiveness and efficiency.

String similarity join is an essential operation in data integration. The era of big data calls for scalable algorithms to support large-scale string similarity joins. In this paper, we study scalable string similarity joins using MapReduce. We propose a MapReduce-based framework, called MASSJOIN, which supports both set-based similarity functions and character-based similarity functions. We extend the existing partition-based signature scheme to support set-based similarity functions. We utilize the signatures to generate key-value pairs. To reduce the transmission cost, we merge key-value pairs to significantly reduce the number of key-value pairs, from cubic to linear complexity, while not sacrificing the pruning power. To improve the performance, we incorporate “light-weight” filter units into the key-value pairs which can be utilized to prune large number of dissimilar pairs without significantly increasing the transmission cost. Experimental results on real-world datasets show that our method significantly outperformed state-of-the-art approaches.