Text-to-3D Scene Retrieval (T3SR) aims to retrieve 3D scenes that match users' linguistic queries, enabling intuitive access to 3D scene repositories. Existing approaches rely on joint embedding learning with large amounts of paired text–scene data, which is expensive to collect and often fails to generalize under open-vocabulary queries and diverse scene distributions. In this paper, we propose Struct-Align, a foundation-model-driven framework for zero-shot T3SR that eliminates the need for paired training data. Our key insight is to reformulate T3SR as a single-modality structural alignment problem by converting both 3D scenes and textual queries into a shared, schema-aligned textual representation compatible with pretrained text embedding models. To reliably derive such representations from complex 3D environments, we introduce a role-decomposed scene structuring pipeline that mitigates generative instability and produces semantically consistent scene depictions. To address the inherent semantic asymmetry between query and scene representations, we further propose a locality-aware structural matching strategy that explicitly localizes query intent and performs instance- and relation-level alignment within query-relevant substructures. Extensive experiments on multiple benchmarks demonstrate that Struct-Align outperforms both training-based and zero-shot baselines while exhibiting strong robustness to domain shift.

A data lake maintains large amounts of heterogeneous data with different data schemas and query interfaces. Efficiently querying and analyzing the heterogeneous data enables users to gain more complete insights. In this article, we study a novel problem of distributed keyword search across heterogeneous data sources. Traditional distributed search algorithms generally require the predefined crossing edges connecting relevant data instances for communication between different sources, which is unpractical for the data lake due to the schema heterogeneity. To effectively perform keyword search over the data lake, we first introduce canonical graphs and then develop a best-first search algorithm called UnifySea, which explores the answers across different sources based on the unified identification of related instances. To further improve the query efficiency, we propose a novel incremental keyword search algorithm called DistSea, which just need to identify the promising relevant data between different sources. DistSea incrementally calculates the optimal answers based on locally partial evaluation. Equipped with several efficient pruning rules, DistSea reduces unpromising tree calculation across different sources. Experimental evaluations on six real-world benchmarks demonstrate the effectiveness, efficiency, and scalability of the proposed algorithms.

Entity matching (EM) aims to identify whether two entities from different data sources refer to the same real-world entity. Most existing cross-modal EM assume that images have simple scenes containing few objects, or do not fully consider the cross-modal knowledge associated with entities. To support more practical application scenarios such as multi-modal knowledge graph integration and visual question answering in data lakes, we introduce our problem of semantic-driven EM across graph and images in this paper. Current semantically matching solutions over cross-modal data face the obstacle of low training efficiency, since their time complexity quadratically grows with the number of entities. To alleviate this issue, we present a novel framework (namely CrossETR) that follows an exploration-then-refinement paradigm. Firstly, a candidate exploration policy is proposed to boost the training efficiency. It explores candidate pairs according to entity correlations and captures structural semantics by adaptive sampling the most informative neighborhood subgraphs. Secondly, the cross-modal entity representations are refined to break modality heterogeneity to support unsupervised matching prediction. Extensive experimental evaluations on three publicly available benchmarks demonstrate the superiority of CrossETR over state-of-the-art approaches in terms of effectiveness and efficiency. Furthermore, a case study highlights that our proposed semantic-driven EM is promising to improve the performance of downstream tasks such as multi-modal knowledge graph integration.

Entity matching (EM) aims to identify equivalent entities across different data sources. Current EM assumes that these data are either homogeneous with aligned schema or heterogeneous but can be transformed into a unified modality. There is an urgent need to consider the entities with different modalities to support practical application scenarios over data lakes such as multi-modal data integration and recommendation system. It is impractical to unify their data modalities. To support EM on heterogeneous entity with different data formats and modalities, we propose cross-modal entity matching in this paper. Inspired by the promising performance achieved by recent pre-trained models, we perform cross-modal entity matching by prompt-tuning pre-trained multi-modal large models (MMLMs) in an unsupervised manner. However, the prompt-tuning faces three challenging issues: (i) objective gap between pre-training and tuning of MMLMs; (ii) data modality gap between the inputs of MMLMs and our matching task; (iii) prompt efficiency on large data. Therefore, we firstly propose a novel EM framework (namely, CrossEM) that addresses cross-modal EM as a matching probability problem with specific prompt-tuning. Secondly, two alternative prompt generation methods are designed to extract structural knowledge from heterogeneous data to overcome the data modality gap with pre-trained models. Thirdly, we present an improved matching framework (namely, CrossEM+) to boost the prompt efficiency on large heterogeneous data. Experimental evaluations verify that our methods significantly outperform the state-of-the-art approaches on three benchmarks. Furthermore, our case study highlights the considerable potential of cross-modal EM in improving the performance of downstream tasks, thereby benefitting a wider range of research areas.

The prevalence of online query services in human life has attracted significant interest from the fields of economics and databases in determining appropriate pricing for such services. Simultaneously, the utilization of graph analytics across various domains has resulted in substantial social and economic benefits in recent years. As the adoption of graph analytics continues to expand, there is a corresponding need to establish fair pricing models for the information contributed by each participant in the data ecosystem. However, current query-based pricing frameworks cannot be applied to price graph statistics, as they fail to consider buyers' affordability and prevent arbitrage trading. To address this gap, in this paper, we propose a novel framework GSHOP for pricing graph statistic queries. Instead of pricing a precise answer for a query, our framework offers the flexibility to price a set of answers injected with noise. Based on the framework, data owners initially create and publish extended local views (ELVs) to represent their graph data. Additionally, it allows buyers to tolerate a certain degree of noise added to the answer to reduce their payments. The framework accurately quantifies the relationship between noise and price to ensure that payment and compensation are reasonable for the buyer and owners, respectively. We also propose algorithms specifically designed for fundamental graph statistics, including node degrees and subgraph counts such as k-stars and k-cliques. Furthermore, we formally prove that the pricing framework is arbitrage-free. Extensive experimental results on real-life graph data validate the good performance of the proposed framework and algorithms.

Cross-source query serves as a proxy for scene understanding to support many web applications such as rec-ommendation systems, e-commerce, and e-learning applications. In this paper, we propose SVQA that semantically combines the knowledge from available images and graphs to answer the complex question. To this end, we design a graph-based method to unify various data sources into one representation. We then develop a complex question parse method that utilizes the structure of languages to transform the query into a query graph. A graph query engine that performs the query graph over the unified data source while optimizing the query process. To evaluate the proposed system, we build a vanilla dataset called MVQA and show that the state-of-the-art (SOTA) VQA models fail to perform our task. The comprehensive evaluations show that the proposed SVQA is able to reason implicit relationships over multiple images and external knowledge to correctly answer a complex query. We hope that our first attempt provides researchers with a fresh taste of multimodal data analysis.

There has been a growing interest in cross-source searching to gain rich knowledge in recent years. A data lake collects massive raw and heterogeneous data with different data schemas and query interfaces. Many real-life applications require query answering over the heterogeneous data lake, such as e-commerce, bioinformatics and healthcare. In this paper, we propose LakeAns that semantically integrates heterogeneous data schemas of the lake to enhance the semantics of query answers. To this end, we propose a novel framework to efficiently and effectively perform the cross-source searching. The framework exploits a reinforcement learning method to semantically integrate the data schemas and further create a global relational schema for the heterogeneous data. It then performs a query answering algorithm based on the global schema to find answers across multiple data sources. We conduct extensive experimental evaluations using real-life data to verify that our approach outperforms existing solutions in terms of effectiveness and efficiency.

In recent years, a surging number of unmanned aerial vehicles (UAVs) are pervasively utilized in many areas. However, the increasing number of UAVs may cause privacy and security issues such as voyeurism and espionage. It is critical for individuals or organizations to manage their behaviors and proactively prevent the misbehaved invasion of unauthorized UAVs through effective anomaly detection. The UAV anomaly detection framework needs to cope with complex signals in the noisy-prone environments and to function with very limited labeled samples. This paper proposesBisSiam, a novel framework that is capable of identifying UAV presence, types and operation modes.BisSiamconverts UAVs signals to bispectrum as the input and exploits a siamese network based contrastive learning model to learn the vector encoding. A sampling mechanism is proposed for optimizing the sample size involved in the model training whilst ensuring the model accuracy without compromising the training efficiency. Finally, we present a similarity-based fingerprint matching mechanism for detecting unseen UAVs without the need of retraining the whole model. Experiment results show that our approach outperforms other baselines and can reach 92.85% accuracy of UAV type detection in unsupervised learning scenarios. 91.4% accuracy can be achieved whenBisSiamis used for detecting the UAV type of the out-of-sample UAVs.

Data is increasingly being bought and sold online, and data market platforms have emerged to facilitate these activities. However, current mechanisms for pricing data mainly focus on traditional relational data. In this paper, we propose a framework GQP for pricing graph data on the data market platform. Specifically, given a set of graph price points and a graph query, we can efficiently compute the price of the query based on the graph price points. We first identify an important property (called arbitrage-free) GQP should satisfy with, such that GQP can effectively price the graph query. We then study the exact pricing problem (NP-completeness) and develop an efficient approximation algorithm to solve the problem. We also study the approximate pricing when the query cannot be answered by price points exactly. Furthermore, to avoid the expensive computing cost of updating graph price points, we study the dynamic query pricing and propose novel solutions to reuse the computed graph price points to reduce the computational complexity. Finally, we use real-life data and synthetic data to experimentally verify that the proposed algorithms are able to effectively and efficiently price large graph data based on the framework GQP.

A data lake is a repository for massive raw and heterogeneous data, which includes multiple data models with different data schemas and query interfaces. Keyword search can extract valuable information for users without the knowledge of underlying schemas and query languages. However, conventional keyword searches are restricted to a certain data model and cannot easily adapt to a data lake. In this paper, we study a novel keyword search. To achieve high accuracy and efficiency, we introduce canonical graphs and then integrate semantically related vertices based on vertex representations. A matching entity based keyword search algorithm is presented to find answers across multiple data sources. Finally, extensive experimental study shows the effectiveness and efficiency of our solution.

Many real-life applications require processing graph data across heterogeneous sources. In this paper, we define the graph federation that indicates that the graph data sources are temporarily federated and offer their data for users. Next, we propose a new framework FedGraph to efficiently and effectively perform subgraph matching, which is a crucial application in graph federation. FedGraph consists of three phases, including query decomposition, distributed matching, and distributed joining. We also develop new efficient approximation algorithms and apply them in each phase to attack the NP-hard problem. The evaluations are conducted in a real test bed using both real-life and synthetic graph datasets. FedGraph outperforms the state-of-the-art methods, reducing the execution time and communication cost by 37.3 × and 61.8 ×, respectively.

We demonstrate AHOM, a system that can Actively Observe Hazards via Monitoring Social Media Streams. AHOM proposes an active way to include the human in the loop of hazard information ac-quisition for social media. Different from state of the art, it supports bi-directional interaction between social media data processing system and social media users, which leads to the establishment of deeper and more accurate situational awareness of hazard events. We demonstrate how AHOM utilizes Twitter streams and bi-directional information exchange with social media users for enhanced hazard observation.

Graph queries have emerged as one of the fundamental techniques to support modern search services, such as PageRank web search, social networking search and knowledge graph search. As such graphs are maintained globally and very huge (e.g., billions of nodes), we need to efficiently process graph queries across multiple geographically distributed datacenters, running geo-distributed graph queries. Existing graph computing frameworks may not work well for geographically distributed datacenters, because they implement a Bulk Synchronous Parallel model that requires excessive inter-datacenter transfers, thereby introducing extremely large latency for query processing. In this paper, we propose GeoGraph --a universal framework to support efficient geo-distributed graph query processing based on clustering datacenters and meta-graph, while reducing the inter-datacenter communication. Our new framework can be applied to many types of graph algorithms without any modification. The framework is developed on the top of Apache Giraph. The experiments were conducted by applying four important graph queries, i.e., shortest path, graph keyword search, subgraph isomorphism and PageRank. The evaluation results show that our proposed framework can achieve up to 82% faster convergence, 42% lower WAN bandwidth usage, and 45% less total monetary cost for the four graph queries, with input graphs stored across ten geo-distributed datacenters.

Containerized web-applications have gained popularity recently due to the advantages provided by the containers including light-weight, packaged, fast start up and shut down and easy scalability. As there are more than 267 cloud providers, finding a flexible deployment option for containerized web-applications is very difficult as each cloud offers numerous deployment infrastructure. Benchmarking is one of the eminent options to evaluate the provisioned resources before product-level deployment. However, benchmarking the massive infrastructure resources provisioned by various cloud providers is a time consuming, tedious and costly process and is not practical to accomplish manually.