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As hardware failures such as node losses become increasingly common, MPI programmers may want to save vulnerable data in a resilient store. While third-party storage solutions such as Redis or the Hazelcast IMap exist, a tailored, MPI-based store may be easier to integrate and can be optimized for particular application needs. This paper considers the implementation of such a store, which is intended as a component in a resilient task-based runtime system written in MPI. The store holds redundant data copies as key-value pairs in the main memories of multiple processes. Since store access operations, such as reads and writes, are naturally one-sided, we implemented the store with passive target MPI RMA functions. Process aborts are detected with the user-level failure mitigation (ULFM) extension of Open MPI. After failures, the program recovers on the surviving processes and continues with the intact data copies. Our implementation proved difficult, since several proposed ULFM functionalities for RMA have not yet been implemented. Even assuming their existence, we think that the programming task could be simplified. This paper describes our experiences, lists functionalities that w

Motivated by recent challenges in the deployment of robots into customer-facing roles within retail, this work introduces a study of customer activity in physical stores as a step toward autonomous understanding of shopper intent. We introduce an algorithm that computes shoppers' ``shelf visits'' -- capturing their browsing behavior in the store. Shelf visits are extracted from trajectories obtained via machine vision-based 3D tracking and overhead cameras. We perform two independent calibrations of the shelf visit algorithm, using distinct sets of trajectories (consisting of 8138 and 15129 trajectories), collected in different stores and labeled by human reviewers. The calibrated models are then evaluated on trajectories held out of the calibration process both from the same store on which calibration was performed and from the other store. An analysis of the results shows that the algorithm can recognize customers' browsing activity when evaluated in an environment different from the one on which calibration was performed. We then use the model to analyze the customers' ``browsing patterns'' on a large set of trajectories and their relation to actual purchases in the stores. Fina

The store language of an automaton is the set of store configurations (state and store contents, but not the input) that can appear as an intermediate step in an accepting computation. A one-way nondeterministic finite-visit Turing machine (fvNTM) is a Turing machine with a one-way read-only input tape, and a single worktape, where there is some number $k$ such that in every accepting computation, each worktape cell is visited at most $k$ times. We show that the store language of every fvNTM is a regular language. Furthermore, we show that the store language of every fvNTM augmented by reversal-bounded counters can be accepted by a machine with only reversal-bounded counters and no worktape. Several applications are given to problems in the areas of verification and fault tolerance, and to the study of right quotients. We also continue the investigation of the store languages of one-way and two-way machine models where we present some conditions under which their store languages are recursive or non-recursive.

The consumer store is ubiquitous and plays an important role in our everyday lives. It is an open question why stores usually have such short life cycles (typically around 3 years in China). This paper proposes a theoretical framework based on an equilibrium in style supply of stores and style demand of consumers to characterize store cash flow (revenue), leading to a strong explanation of this puzzle. In our model, we derive that the preference shifting of consumers is the main reason for the cash flow decreasing to its break-even line over time, while the visibility broadening leads to initial growth, resulting in rainbow-shaped cash flow and its life cycle. Moreover, the intensified spatial competition will lead to an unexpected decrease in the store's cash flow, or even closure. We calibrate our model with proprietary data of three Chinese stores from three representative industries and study the relationship between customers' preference shifting and cash flow. To our knowledge, there have been no prior attempts to quantitatively model the life cycle of the store.

Key-value stores are a fundamental class of NoSQL databases that offer a simple yet powerful model for data storage and retrieval, representing information as pairs of unique keys and associated values. Their minimal structure enables exceptionally fast access times, scalability, and flexibility in storing diverse data types, making them ideal for high-performance applications such as caching, session management, and distributed systems. As modern computing increasingly demands responsiveness and scalability, key-value stores have become a critical component of the data infrastructure in both industry and research contexts. In this work, we present FlashMap, a high-performance key-value store optimized for Flash-based solid-state drives (SSDs). Experiments show that FlashMap achieves outstanding throughput, averaging 19.8 million inserts and 23.8 million random lookups per second with a 100-byte payload, all on a single data center-grade server.

To effectively manage increasing knowledge graphs in various domains, a hot research topic, knowledge graph storage management, has emerged. Existing methods are classified to relational stores and native graph stores. Relational stores are able to store large-scale knowledge graphs and convenient in updating knowledge, but the query performance weakens obviously when the selectivity of a knowledge graph query is large. Native graph stores are efficient in processing complex knowledge graph queries due to its index-free adjacent property, but they are inapplicable to manage a large-scale knowledge graph due to limited storage budgets or inflexible updating process. Motivated by this, we propose a dual-store structure which leverages a graph store to accelerate the complex query process in the relational store. However, it is challenging to determine what data to transfer from relational store to graph store at what time. To address this problem, we formulate it as a Markov Decision Process and derive a physical design tuner DOTIL based on reinforcement learning. With DOTIL, the dual-store structure is adaptive to dynamic changing workloads. Experimental results on real knowledge gr

We propose a method that can generate customer trajectories and purchasing behaviors in retail stores simultaneously using Transformer-based deep learning structure. Utilizing customer trajectory data, layout diagrams, and retail scanner data obtained from a retail store, we trained a GPT-2 architecture from scratch to generate indoor trajectories and purchase actions. Additionally, we explored the effectiveness of fine-tuning the pre-trained model with data from another store. Results demonstrate that our method reproduces in-store trajectories and purchase behaviors more accurately than LSTM and SVM models, with fine-tuning significantly reducing the required training data.

As a pivotal extension of the renowned ChatGPT, the GPT Store serves as a dynamic marketplace for various Generative Pre-trained Transformer (GPT) models, shaping the frontier of conversational AI. This paper presents an in-depth measurement study of the GPT Store, with a focus on the categorization of GPTs by topic, factors influencing GPT popularity, and the potential security risks. Our investigation starts with assessing the categorization of GPTs in the GPT Store, analyzing how they are organized by topics, and evaluating the effectiveness of the classification system. We then examine the factors that affect the popularity of specific GPTs, looking into user preferences, algorithmic influences, and market trends. Finally, the study delves into the security risks of the GPT Store, identifying potential threats and evaluating the robustness of existing security measures. This study offers a detailed overview of the GPT Store's current state, shedding light on its operational dynamics and user interaction patterns. Our findings aim to enhance understanding of the GPT ecosystem, providing valuable insights for future research, development, and policy-making in generative AI.

A non-fungible token (NFT) references a data store location, typically, using a URL or another unique identifier. At the minimum, a NFT is expected to guarantee ownership and control over the tokenised asset. However, information stored on a third party data store may be copied and stolen. We propose a solution to give control back to the information owner by storing encrypted content on the data store and providing additional security against hacks and zero day exploits. The content on our data store is never decrypted or returned to its owner for decryption during rekeying. Also, the key size in our protocol does not increase with each rekeying. With this, we reduce the synchronisation steps and maintain a bounded key size.

Growing competitiveness and increasing availability of data is generating tremendous interest in data-driven analytics across industries. In the retail sector, stores need targeted guidance to improve both the efficiency and effectiveness of individual stores based on their specific locations, demographics, and environment. We propose an effective data-driven framework for internal benchmarking that can lead to targeted guidance for individual stores. In particular, we propose an objective method for segmenting stores using a model-based clustering technique that accounts for similarity in store performance dynamics. The proposed method relies on an effective Finite Mixture of Regressions technique based on competitive learning for carrying out the model-based clustering with `must-link' constraints and modeling store performance. We also propose an optimization framework to derive tailored recommendations for individual stores within store clusters that jointly improves profitability for the store while also improving sales to satisfy franchiser requirements. We validate the methods using synthetic experiments as well as a real-world automotive dealership network study for a leadi

Understanding customer behavior in retail stores plays a crucial role in improving customer satisfaction by adding personalized value to services. Behavior analysis reveals both general and detailed patterns in the interaction of customers with a store items and other people, providing store managers with insight into customer preferences. Several solutions aim to utilize this data by recognizing specific behaviors through statistical visualization. However, current approaches are limited to the analysis of small customer behavior sets, utilizing conventional methods to detect behaviors. They do not use deep learning techniques such as deep neural networks, which are powerful methods in the field of computer vision. Furthermore, these methods provide limited figures when visualizing the behavioral data acquired by the system. In this study, we propose a framework that includes three primary parts: mathematical modeling of customer behaviors, behavior analysis using an efficient deep learning based system, and individual and group behavior visualization. Each module and the entire system were validated using data from actual situations in a retail store.

Retailers use the Vector AutoRegressive (VAR) model as a standard tool to estimate the effects of prices, promotions and sales in one product category on the sales of another product category. Besides, these price, promotion and sales data are available for not just one store, but a whole chain of stores. We propose to study cross-category effects using a multi-class VAR model: we jointly estimate cross-category effects for several distinct but related VAR models, one for each store. Our methodology encourages effects to be similar across stores, while still allowing for small differences between stores to account for store heterogeneity. Moreover, our estimator is sparse: unimportant effects are estimated as exactly zero, which facilitates the interpretation of the results. A simulation study shows that the proposed multi-class estimator improves estimation accuracy by borrowing strength across classes. Finally, we provide three visual tools showing (i) the clustering of stores on identical cross-category effects, (ii) the networks of product categories and (iii) the similarity matrices of shared cross-category effects across stores.

With the SAP HANA database, SAP offers a high-performance in-memory hybrid-store database. Hybrid-store databases---that is, databases supporting row- and column-oriented data management---are getting more and more prominent. While the columnar management offers high-performance capabilities for analyzing large quantities of data, the row-oriented store can handle transactional point queries as well as inserts and updates more efficiently. To effectively take advantage of both stores at the same time the novel question whether to store the given data row- or column-oriented arises. We tackle this problem with a storage advisor tool that supports database administrators at this decision. Our proposed storage advisor recommends the optimal store based on data and query characteristics; its core is a cost model to estimate and compare query execution times for the different stores. Besides a per-table decision, our tool also considers to horizontally and vertically partition the data and manage the partitions on different stores. We evaluated the storage advisor for the use in the SAP HANA database; we show the recommendation quality as well as the benefit of having the data in the op

In the last decade, document store database systems have gained more traction for storing and querying large volumes of semi-structured data. However, the flexibility of the document stores' data models has limited their ability to store data in a columnar-major layout - making them less performant for analytical workloads than column store relational databases. In this paper, we propose several techniques based on piggy-backing on Log-Structured Merge (LSM) tree events and tailored to document stores to store document data in a columnar layout. We first extend the Dremel format, a popular on-disk columnar format for semi-structured data, to comply with document stores' flexible data model. We then introduce two columnar layouts for organizing and storing data in LSM-based storage. We also highlight the potential of using query compilation techniques for document stores, where values' types are known only at runtime. We have implemented and evaluated our techniques to measure their impact on storage, data ingestion, and query performance in Apache AsterixDB. Our experiments show significant performance gains, improving the query execution time by orders of magnitude while minimally

Companies are using machine learning to solve real-world problems and are developing hundreds to thousands of features in the process. They are building feature engineering pipelines as part of MLOps life cycle to transform data from various data sources and materialize the same for future consumption. Without feature stores, different teams across various business groups would maintain the above process independently, which can lead to conflicting and duplicated features in the system. Data scientists find it hard to search for and reuse existing features and it is painful to maintain version control. Furthermore, feature correctness violations related to online (inferencing) - offline (training) skews and data leakage are common. Although the machine learning community has extensively discussed the need for feature stores and their purpose, this paper aims to capture the core architectural components that make up a managed feature store and to share the design learning in building such a system.

Resource Description Framework (RDF) and Property Graph (PG) are the two most commonly used data models for representing, storing, and querying graph data. We present Expressive Reasoning Graph Store (ERGS) -- a graph store built on top of JanusGraph (a Property Graph store) that also allows storing and querying of RDF datasets. First, we describe how RDF data can be translated into a Property Graph representation and then describe a query translation module that converts SPARQL queries into a series of Gremlin traversals. The converters and translators thus developed can allow any Apache Tinkerpop compliant graph database to store and query RDF datasets. We demonstrate the effectiveness of our proposed approach using JanusGraph as the base Property Graph store and compare its performance with standard RDF systems.

It is well known that the "store language" of every pushdown automaton -- the set of store configurations (state and stack contents) that can appear as an intermediate step in accepting computations -- is a regular language. Here many models of language acceptors with various data structures are examined, along with a study of their store languages. For each model, an attempt is made to find the simplest model that accepts their store languages. Some connections between store languages of one-way and two-way machines generally are demonstrated, as with connections between nondeterministic and deterministic machines. A nice application of these store language results is also presented, showing a general technique for proving families accepted by many deterministic models are closed under right quotient with regular languages, resolving some open questions (and significantly simplifying proofs for others that are known) in the literature. Lower bounds on the space complexity for recognizing store languages for the languages to be non-regular are obtained.

Data prefetching aims to improve access times to data storage systems by predicting data records that are likely to be accessed by subsequent requests and retrieving them into a memory cache before they are needed. In the case of Persistent Object Stores, previous approaches to prefetching have been based on predictions made through analysis of the store's schema, which generates rigid predictions, or monitoring access patterns to the store while applications are executed, which introduces memory and/or computation overhead. In this paper, we present CAPre, a novel prefetching system for Persistent Object Stores based on static code analysis of object-oriented applications. CAPre generates the predictions at compile-time and does not introduce any overhead to the application execution. Moreover, CAPre is able to predict large amounts of objects that will be accessed in the near future, thus enabling the object store to perform parallel prefetching if the objects are distributed, in a much more aggressive way than in schema-based prediction algorithms. We integrate CAPre into a distributed Persistent Object Store and run a series of experiments that show that it can reduce the execu

Modern CDCL SAT solvers learn clauses rapidly, and an important heuristic is the clause deletion scheme. Most current solvers have two (or more) stores of clauses. One has ``valuable'' clauses which are never deleted. Most learned clauses are added to the other, with an aggressive deletion strategy to restrict its size. Recent solvers in the MapleSAT family, have comparatively complex deletion scheme, and perform well. Many solvers store only binary clauses permanently, but MapleLCMDistChronoBT stores clauses with small LBD permanently. We report an experimental study of the permanent clause store in MapleLCMDistChronoBT. We observe that this store can get quite large, but several methods for limiting its size reduced performance. We also show that alternate size and LBD based criteria improve performance, while still having large permanent stores. In particular, saving clauses up to size 8, and adding small numbers of high-centrality clauses, both improved performance, with the best improvement using both methods.