Sliding window aggregation is a core operation in data stream analysis that extracts summaries from the most recent data stream. An evict or insert of the window can be handled in$O(1)$for in-order data streams. However, real-world data streams are typically disordered due to network delays. To process out-of-order data streams, existing methods primarily use a tree to maintain the sliding windows. Since the complexity of the tree is related to the window size, the performance of these methods will drop sharply or become unavailable when facing a big window. To overcome the limitation of existing methods, this paper presents Finger B-Trees Forest Aggregation (FIFA). This novel forest-based sliding window aggregation scheme optimally handles out-of-order data streams. At its heart, FIFA uses aggregation forest to extend Finger B-Trees. Specifically, FIFA evenly divides a window into several chunks and constructs a separate tree to maintain each chunk. When an out-of-order item arrives, FIFA first locates the corresponding chunk of the item and then uses Finger B-Trees to insert it into the window efficiently. Chunking reduces the complexity of the tree and the coupling of aggregation results by isolating items within windows. Thus, an insert or evict takes amortized$O(c)$in the worst case, where$c$is the size of each chunk. Finally, extensive experiments based on real-world data demonstrate that FIFA achieves an average 2-fold throughput improvement on out-of-order data streams compared with the state-of-the-art (SOTA) aggregation schemes.

Recently, many laws and regulations have granted users the right to be forgotten, i.e., the right to require data controllers to delete user data. Various methods for machine unlearning have been proposed to remove individual data points. However, they do not scale to the scenarios where larger groups of features are to be removed. To address this challenge, we propose MAFRO, an optimal-granularity fuzzy decision rule–based classifier that accelerates unlearning via influence functions. Building on granular computing (GrC), MAFRO first selects a minimal reduct of attributes, then constructs fuzzy granules with a Gaussian membership function to extract concise decision rules and realizes unlearning through the influence function. Specifically, instead of training with the full set of attributes, we use the reduct, a minimal subset of attributes that can classify the data with the same accuracy as the full set of attributes. Next, we extract fuzzy rules based on the reduct. Finally, fusing the generated rules establishes the linear model with strongly convex loss functions. In this way, MAFRO can quantify the divergence caused by attribute deleting and update the model without retraining it, thereby adapting the influence of data removal on the model and accelerating the unlearning process. We conduct extensive experiments to evaluate MAFRO on ten typical datasets in terms of performance and unlearning speed. We compare MAFRO with the state-of-theart algorithms. Experimental results demonstrate that MAFRO enhances accuracy by an average of 6.96%, and achieves up to 236× speedup for attribute unlearning tasks.

Sliding window aggregation, which extracts summaries from data streams, is a core operation in streaming analysis. Though existing sliding window algorithms that perform single eviction and insertion operations can achieve a worst-case time complexity of$O(1)$for in-order streams, real-world data streams often involve out-of-order data and exhibit burst data characteristics, which pose performance challenges to these sliding window algorithms. To address this challenging issue, we proposeGecko- a novel sliding window aggregation algorithm that supports bulk eviction. Gecko leverages a granular-based eviction strategy for various bulk sizes, enabling efficient bulk eviction while maintaining the performance close to that of in-order stream algorithms for single evictions. For large data bulks, Gecko performs coarse-grained eviction at the chunk level, followed by fine-grained eviction using leftward binary tree aggregation (LTA) as a complementary method. Moreover, Gecko partitions data based on chunks to prevent the impacts of out-of-order data on other chunks, thereby enabling efficient handling of out-of-order data streams. We conduct extensive experiments to evaluate the performance of Gecko. Experimental results demonstrate that Gecko exhibits superior performance over other solutions, which is consistent with theoretical expectations. In real-world data scenarios, Gecko improves the average throughput of the state-of-the-art algorithm b_FiBA by 1.7 times, with a maximum improvement of up to 3.5 times. Gecko also demonstrates the best latency performance among all compared schemes.

Edge servers usually store collected data in cloud servers and use deduplication techniques to remove redundancy. However, edge servers can also perform deduplication during data collection. This requires transferring fingerprints from the cloud servers to the edge servers for assistance. Since the large volume of fingerprint data on the cloud server, for example, 1 PB of data corresponds to 8 TB of fingerprints, transferring all fingerprints to the edge servers is impractical. Therefore, we propose a fingerprint offloading strategy. Only a small amount of fingerprints and data chunks needed for edge deduplication are offloaded from the cloud server to the edge server, enabling cloud-edge collaborative deduplication. The general process is as follows: First, the edge server collect a large amount of data from various devices, divides the data into chunks, and calculates fingerprints to identify unique data chunks and fingerprints. Then, the edge server upload the fingerprints to the cloud server. The cloud server check the fingerprints and offload the data chunks corresponding to existing fingerprints back to the edge server. Upon receiving these data chunks, the edge server performs thorough deduplication. Finally, the deduplicated data chunks are uploaded to the cloud server, ensuring that only unique data is transmitted to the cloud server. Experiments used chunks ranging from 1 KB to 16 KB, with an average size of 4 KB, and employed three real backup datasets. The results showed that the edge server computation time was reduced by 48.1%, metadata storage was reduced by 98.1%, and the upload volume from the edge server to the cloud server decreased by 87.1%. The size of fingerprints and data chunks offloaded to the edge server ranged from 9.4 MB to 1624.2 MB.

Information granules are effective in revealing the structure of data. Therefore, it is a common practice in data mining to use information granules for classifying datasets. In the existing granular classifiers, the information granules are often classified according to the standard membership function only without considering the influence of different feature weights on the quality of granules and label classification results. In this article, we utilize the feature weighting of data to produce the information granules with high consistency rate called FIG. Firstly, we use consistency rate and contribution scores to generate information granules. Then, we propose a granular two-stage classifier GTC based on FIG. GTC divides the data into fuzzy and fixed points and then calculates the interval matching degree to assign data points to the most suitable cluster in the second step. Finally, we compare FIG with two state-of-the-art granular models (T-GrM and FGC-rule), and classification accuracy is also compared with other classification algorithms. The extensive experiments on synthetic datasets and public datasets from UCI show that FIG has sufficient performance to describe the data structure and excellent capability under the constructed granular classifier GTC. Compared with T-GrM and FGC-rule, the time overhead required for FIG to obtain information granules is reduced by an average of 51.07%, the per unit quality of the granules is also increased by more than 14.74%. Compared with other classification algorithms, an average of 5.04% improves GTC accuracy.

Read-disturb problem plays a pivotal factor in the performance of NAND flash memory, because it deteriorates the read-disturb errors of NAND flash. Although ECC, read retry, and read reclaim technologies are designed to correct read-disturb errors, these techniques drastically increase read latency and degrade read performance. Moreover, modern SSDs implement a buffer in the built-in DRAM to store frequently accessed data, which can cache hot read data to alleviate the read-disturb problem. Unfortunately, the buffer primarily serves write requests to curtail write operations in flash memory, and ignores the ever-increasing requirement from users for read latency. To address this issue, we propose a read-disturb-aware dynamic buffer management scheme called Minato that reduces read-disturb errors with rationally read buffer management, aiming to improve the read performance of SSDs. Minato includes two distinctive and vital features. First, Minato dynamically adjusts the size of the read buffer and write buffer through the hit situation of requests, thus increasing the size of the read buffer while maintaining the write hit of the write buffer. Second, to further reduce read-disturb errors, Minato implements a read buffer filter to preferentially cache hot read data disturbing more valid pages into the read buffer. We compare Minato with two state-of-art schemes -BPLRU and GCaR in terms of write hit ratio, read-disturb counts, and read/write response time. The experimental results derived from nine real-world workload traces show that Minato efficiently alleviates the read-disturb problem of flash memory without affecting the write hit ratio, and significantly improves read/write performance. In particular, compared with the existing schemes, Minato slashes the read/write response time by an average of 34.6%.

Information granule is a classic mathematical paradigm in the field of data mining. Existing research focuses on improving granule quality to optimize models. However, in these studies, they do not consider that the fairness of the granular model will affect the performance of the granule, especially in some severe social issues (Law, Finance, Education, and more), often due to the participation of sensitive features, the application results of the granule (such as classification) cause population bias. Thus, we construct an adaptive probability replacement resampling model (APR) in Pre-Processors to reduce the bias of sensitive features on granular results. Then, in In-Processors, we add fairness optimizations (FO) to improve the fairness of information granules. Finally, we propose a fair information granule model based on adaptive probability replacement resampling called APRG(APR+FO). We select three loan datasets to verify the feasibility of the granular model according to the current sensitive lending issues in the financial field. The experimental results show that compared with the existing granular models, our proposed method dramatically improves the fairness of the constructed granular models, especially the APRG model, which has an average increase of 46.6% on Demographic Parity (DP) and 77.9% on Equalized Odds (EO). Compared with other existing granular models, the granule quality of APRG is increased by 38.88% on average; in terms of classification accuracy, the average decrease is only 2.03%.