Semi-supervised learning uses labeled and unlabeled data from known classes for training, assuming the test data contains only those classes. However, in real-world scenarios, new classes can appear. Generalized Category Discovery (GCD) extends SSL to handle unlabeled samples that may belong to both known and unknown categories. The challenge arises from the lack of prior information about the unknown categories. We propose to generate unknown samples to address the GCD problem, called Generalized Category Discovery with Unknown Sample Generation (GCDUSG). Since the number of unknown categories is uncertain, we propose a prototype alignment method to estimate both the class numbers and pseudo-labels for unlabeled samples, thereby enabling us to learn the unknown prototypes. We have developed a process for generating realistic and discriminative unknown samples based on the known-unknown relationships between known and unknown prototypes. We generate realistic and discriminative unknown samples leveraging the known-unknown relationships. We achieve this by minimizing the class-wise Maximum Mean Discrepancy distance between the generated samples and the selected unknown samples. To account for the pseudo-labels assigned to unlabeled samples, we train a classifier using all samples, incorporating a pseudo-label supervision loss to mitigate the impact of potentially erroneous labels. This comprehensive training equips the classifier to effectively handle both known and unknown classes during testing. Extensive experiments conducted on benchmark datasets demonstrate the effectiveness of our approach.

Directly performing operations on compressed data has been proven to be a big success facing Big Data problems in modern data management systems. These systems have demonstrated significant compression benefits and performance improvement for data analytics applications. However, current systems only focus on data queries, while a complete Big Data system must support both data query and data manipulation. To solve this problem, we develop CompressDB, which is a new storage engine that can support data processing for databases without decompression. CompressDB has the following advantages. First, CompressDB utilizes context-free grammar to compress data, and supports both data query and data manipulation. Second, for adaptability, we integrate CompressDB to file systems so that a wide range of databases can directly use CompressDB without any change. Third, we enable operation pushdown to storage so that we can perform data query and manipulation in storage systems without bringing large data to memory for high efficiency. We validate the efficacy of CompressDB supporting various kinds of database systems, including SQLite, MySQL, LevelDB, MongoDB, ClickHouse, and Neo4j. We evaluate our method using seven real-world datasets with various lengths, structures, and content in both single node and cluster environments. Experiments show that CompressDB achieves 40% throughput improvement and 44% latency reduction, along with 1.75 compression ratio on average.

Snapshot isolation (SI) is a prevalent weak isolation level that avoids the performance penalty imposed by serializability and simultaneously prevents various undesired data anomalies. Nevertheless, SI anomalies have recently been found in production cloud databases that claim to provide the SI guarantee. Given the complex and often unavailable internals of such databases, a black-box SI checker is highly desirable. In this paper we present PolySI, a black-box checker that efficiently checks SI and provides understandable counterexamples upon detecting violations. PolySI builds on a characterization of SI using generalized polygraphs (GPs), for which we establish its soundness and completeness. PolySI employs an SMT solver and also accelerates SMT solving by utilizing a compact constraint encoding of GPs and domain-specific optimizations for pruning constraints. As our extensive assessment demonstrates, PolySI successfully reproduces all of 2477 known SI anomalies, detects novel SI violations in three production cloud databases, identifies their causes, outperforms the state-of-the-art black-box checkers under a wide range of workloads, and can scale up to large workloads.

The adoption of temporal expressions into SQL:2011 has continuously driven the extensions of temporal support in relational database systems (a.b.a. RDBMSs). In this paper, we present T-SQL, a lightweight yet efficient built-in temporal implementation in RDBMSs. T-SQL completely relies on multi-version concurrency control (MVCC) which is widely adopted in RDMBSs to manage temporal data. For temporal data, current records are maintained in legacy databases, and historical records, i.e., previoius versions of current records (if any), which used to be periodically reclaimed are separately maintained in KV stores. To enable temporal query processing under SQL:2011, we extend the query engine in legacy RDBMSs to support query processing over either historical records or current records or both. Further, regarding temporal data are ever-increasing, we propose various optimizations to reduce the storage overhead of KV stores while keeping efficient query performance. We elaborate on a publicly available implementation, on how to integrate T-SQL into both centralized and distributed RDBMSs. We conduct extensive experiments on both YCSB and TPC-series benchmarks by comparing T-SQL with other temporal database systems. The results show that T-SQL is both lightweight and efficient.

In decentralized database systems, it is reported that serializability could still produce unexpected transaction orderings, leading to the stale read anomaly. To eliminate this anomaly, strict serializability imposes an additional ordering constraint, called the real-time order, which is required to be preserved among serializable transactions. Yet, preserving the real-time order in strict serializability often causes the performance to drop significantly. Because a weaker data consistency often yields better performance, in this paper, we model serializability from different consistency perspectives to properly leverage the performance and consistency. To do this, we first define a group of orderings, based on which we formulate multi-level serializability by preserving a certain set of ordering constraints among transactions. We then propose a bidirectional timestamp adjustment algorithm (abbreviated as BDTA) to support multi-level serializability with various optimizations. Our special design makes ordering constraints among transactions be preserved simply by adjusting timestamp intervals. Finally, we conduct extensive experiments to show the necessity of introducing multi-level serializability and confirm that BDTA achieves up to 1.19 × better performance than the state-of-the-art concurrency control algorithms.

In modern data management systems, directly performing operations on compressed data has been proven to be a big success facing big data problems. These systems have demonstrated significant compression benefits and performance improvement for data analytics applications. However, current systems only focus on data queries, while a complete big data system must support both data query and data manipulation.

Shadow has gradually become an important feature in current synthetic aperture radar interpretation, while shadow detection itself seemingly didn't attract enough attention in the past research. This paper contributes to propose a new method for shadow detection in SAR images. Our algorithm is divided into three stages: firstly, suspected shadow areas are extracted from the input SAR image with a dual-threshold OTSU based segmentation; secondly, an improved two parameter-constant false alarm rate method is utilized to detect objects; at last, we design a discrimination strategy to remove false areas from suspected shadows, and then the left regions are final shadow detection results. Experiments based on images from MSTAR dataset present that our method comprehensively outperforms another published algorithm, WD-CFAR, which demonstrates the feasibility of applying our algorithm to practical SAR shadow detection tasks.

Object detection is a significant step in synthetic aperture radar (SAR) image interpretation. This paper contributes to propose a saliency method based on principle component analysis (PCA) and global contrast for SAR object detection. Firstly, the original SAR image is smoothed by Gaussian filter and then segmented into superpixels by the linear iterative cluster (SLIC) algorithm. Secondly, two feature maps are created based on PCA and global contrast respectively, and are fused together to construct the saliency map. Lastly, we adopt an adaptive threshold to segment the saliency map and extract object areas. Experimental results on real SAR images indicate that the constructed saliency map is able to highlight objects and inhibit other areas effectively. Moreover, the proposed method performs well in both detection accuracy and time cost.

Driven by the recent adoption of temporal expressions into SQL:2011, extensions of temporal support in conventional database management systems (a.b.a. DBMSs) have re-emerged as a research hotspot. In this paper, we present a lightweight yet efficient built-in temporal implementation in Tencent's distributed database management system, namely TDSQL. The novelty of TDSQL's temporal implementation includes: (1) a new temporal data model with the extension of SQL:2011, (2) a built-in temporal implementation with various optimizations, which are also applicable to other DBMSs, and (3) a low-storage-consumption in which only data changes are maintained. For the repeatability purpose, we elaborate the integration of our proposed techniques into MySQL. We conduct extensive experiments on both real-life dataset and synthetic TPC benchmarks by comparing TD-SQL with other temporal databases. The results show that TDSQL is lightweight and efficient.

Attention model and saliency detection has been widely researched in computer vision. This paper contributes to present a novel saliency-based detection algorithm for synthetic aperture radar (SAR) interpretation. Firstly, the input image is segmented into superpixels by simple linear iterative cluster (SLIC) process. Secondly, three features maps are created based on standard deviation, peak value and comparison with background, respectively, and the final saliency map is the fusion of the three feature maps. Lastly, a global threshold is utilized to segment the saliency map and extract target regions. Experimental results with real SAR images demonstrate that the produced saliency map is effective on both highlighting targets and inhibiting background. The proposed method also shows better detection performance and less time-consuming than the conventional CFAR.

Similarity search is a primitive operation in various database applications. Thus far, a large number of access methods have been proposed to accelerate the similarity query processing. Nonetheless, these methods mostly focus on developing standalone systems by proposing new indices. Given the fact that existing RDBMS merely support traditional indices, it is of great necessity and practical importance to develop a standard RDBMS built-in index based approach to speeding up the query processing. In this demonstration, we introduce MSQL+, a plugin toolkit that enable users to answer similarity queries in metric spaces simply using standard SQL statements. This toolkit can help existing RDBMS to effectively and efficiently handle with big data due to the following three advantages. First, MSQL+ enables users to find similar objects by submitting SELECT-FROM-WHERE statements so that it can be easily integrated into existing RDBMS. Second, MSQL+ works in a more general data space. Objects of any type can be indexed by B + -trees and the query processing can be boosted by using index seeks, as long as the similarity function is metric. Third, MSQL+ supports the parallelization of both pre-processing and query processing in distributed RDBMS.