Indexes are crucial for database performance. Index tuning, i.e., selecting appropriate indexes for a database workload, is an important problem. The state-of-the-art index tuning tools in the industry, e.g., Database Tuning Advisor (DTA) developed for Microsoft SQL Server, rely on a "what-if" API, which can estimate the cost of a query for a given index configuration. They take as input a SQL workload and constraints such as a storage bound, and search over the large space of index configurations to find one with low optimizer-estimated cost for the input workload. Large language models (LLMs) offer a different approach to index tuning, using knowledge they have learned from publicly available training data. However, the effectiveness of LLM-driven index tuning in comparison to today's index advisors, particularly on enterprise workloads, remains unclear. In this paper, we study the practical effectiveness of LLM-driven index tuning on Microsoft SQL Server using both industrial benchmarks and real-world enterprise customer workloads, and compare it with DTA. Our results show that while LLMs in several cases identify configurations that significantly outperform those found by DTA i
Defending against today's increasingly sophisticated cyberattacks requires security analysts to continuously translate evolving attacker tradecraft into detection logic. This places defenders in a reactive posture, requiring constantly updated expertise across an increasingly fragmented security landscape. We introduce the Dynamic Threat Detection Agent (DTDA), an always-on adaptive agent that continuously investigates security incidents across Microsoft Defender to uncover hidden threats and generate explainable detections when attack-story gaps are found. DTDA combines: (1) a unified activity timeline spanning alerts, events, user and entity behavior analytics, and threat intelligence; (2) versioned LLM prompt contracts with schema validation, grounding requirements, bounded retries, and fail-closed suppression; (3) a planner-executor investigation loop that generates attack-specific hypotheses and gathers supporting and refuting evidence; and (4) dynamic alert generation with a context-relevant title, severity, MITRE mappings, remediation guidance, implicated entities, and natural-language attack description. Integrated into Microsoft Security Copilot and deployed across tens of
The study analyzes the introduction of Microsoft 365 Copilot in a non-university research organization using a repeated cross-sectional employee survey. We assess usefulness, ease of use, output quality and reliability, and usefulness for typical knowledge-work activities. Administrative staff report higher usefulness and reliability, whereas scientific staff develop more positive assessments over time, especially regarding productivity and workload reduction. Copilot is widely viewed as user-friendly and technically reliable, with greatest added value for clearly structured, text-based tasks. The findings highlight learning and routinization effects when embedding generative AI into work processes and stress the need for context-sensitive implementation, role-specific training and governance to foster sustainable acceptance of generative AI in knowledge-intensive organizations.
The topological gap protocol (TGP) is presented as "a series of stringent experimental tests" for the presence of topological superconductivity and associated Majorana bound states. Here, we show that the TGP, 'passed' by Microsoft Quantum [PRB 107, 245423 (2023)], lacks a consistent definition of 'gap' or 'topological', and even utilises different parameters when applied to theoretical simulations compared to experimental data. Furthermore, the TGP's outcome is sensitive to the choice of magnetic field range, bias voltage range, data resolution, and number of cutter voltage pairs - data parameters that, in PRB 107, 245423 (2023), vary significantly, even for measurements of the same device. As a result, the core claims of PRB 107, 245423 (2023) are primarily based on unexplained measurement choices and inconsistent definitions, rather than on intrinsic properties of the studied devices. As such, Microsoft Quantum's claim in PRB 107, 245423 (2023) that their devices have a "high probability of being in the topological phase" is not reliable and must be revisited. Our findings also suggest that subsequent studies, e.g. Nature 638, 651-655 (2025), that are based on tuning up devices
Microsoft Teams is a collaboration and communication platform developed by Microsoft that replaces and extends Microsoft Skype for Business. It differs from Skype for Business by the fact that it exists only as part of the Microsoft 365 products whereas Skype for Business can be deployed completely or partly on-premise. During the pandemic emergency in 2020 and 2021 Microsoft Teams has increased dramatically its base of users as most of the meetings and the communications had to be conducted in virtual environments by users working remotely. Microsoft Teams allows users to collaborate sending and sharing information virtually with anyone internal or external to the an organization with PCs and mobile devices, therefore it requires a careful review of all the security configurations and procedures within the organization. Microsoft Teams infrastructure can also be integrated with PSTN telephone services, natively within the Microsoft 365 services or by integrating other PSTN service providers. Therefore, its architecture extends the perimeter that could be exploited for an attack. Microsoft Teams features can also be extended by Apps. There are hundreds of Apps developed by Microsof
Windows OS is facing a huge rise in kernel attacks. An overview of popular techniques that result in loading kernel drivers will be presented. One of the key targets of modern threats is disabling and blinding Microsoft Defender, a default Windows AV. The analysis of recent driver-based attacks will be given, the challenge is to block them. The survey of user- and kernel-level attacks on Microsoft Defender will be given. One of the recently published attackers techniques abuses Mandatory Integrity Control (MIC) and Security Reference Monitor (SRM) by modifying Integrity Level and Debug Privileges for the Microsoft Defender via syscalls. However, this user-mode attack can be blocked via the Windows 'trust labels' mechanism. The presented paper discovered the internals of MIC and SRM, including the analysis of Microsoft Defender during malware detection. We show how attackers can attack Microsoft Defender using a kernel-mode driver. This driver modifies the fields of the Token structure allocated for the Microsoft Defender application. The presented attack resulted in disabling Microsoft Defender, without terminating any of its processes and without triggering any Windows security fe
This study explores the integration of the ChatGPT API with GPT-4 model and Microsoft Copilot Studio on the Microsoft Teams platform to develop an intelligent tutoring system. Designed to provide instant support to students, the system dynamically adjusts educational content in response to the learners' progress and feedback. Utilizing advancements in natural language processing and machine learning, it interprets student inquiries, offers tailored feedback, and facilitates the educational journey. Initial implementation highlights the system's potential in boosting students' motivation and engagement, while equipping educators with critical insights into the learning process, thus promoting tailored educational experiences and enhancing instructional effectiveness.
Security operation centers contend with a constant stream of security incidents, ranging from straightforward to highly complex. To address this, we developed Microsoft Copilot for Security Guided Response (CGR), an industry-scale ML architecture that guides security analysts across three key tasks -- (1) investigation, providing essential historical context by identifying similar incidents; (2) triaging to ascertain the nature of the incident -- whether it is a true positive, false positive, or benign positive; and (3) remediation, recommending tailored containment actions. CGR is integrated into the Microsoft Defender XDR product and deployed worldwide, generating millions of recommendations across thousands of customers. Our extensive evaluation, incorporating internal evaluation, collaboration with security experts, and customer feedback, demonstrates that CGR delivers high-quality recommendations across all three tasks. We provide a comprehensive overview of the CGR architecture, setting a precedent as the first cybersecurity company to openly discuss these capabilities in such depth. Additionally, we release GUIDE, the largest public collection of real-world security incident
Despite recent evidence that Microsoft Academic is an extensive source of citation counts for journal articles, it is not known if the same is true for academic books. This paper fills this gap by comparing citations to 16,463 books from 2013-2016 in the Book Citation Index (BKCI) against automatically extracted citations from Microsoft Academic and Google Books in 17 fields. About 60% of the BKCI books had records in Microsoft Academic, varying by year and field. Citation counts from Microsoft Academic were 1.5 to 3.6 times higher than from BKCI in nine subject areas across all years for books indexed by both. Microsoft Academic found more citations than BKCI because it indexes more scholarly publications and combines citations to different editions and chapters. In contrast, BKCI only found more citations than Microsoft Academic for books in three fields from 2013-2014. Microsoft Academic also found more citations than Google Books in six fields for all years. Thus, Microsoft Academic may be a useful source for the impact assessment of books when comprehensive coverage is not essential.
In this Reply we respond to the comment by Antipov et al. from Microsoft Quantum on Hess et al., PRL 130, 207001 (2023). Antipov et al. reported only a single simulation and claimed it did not pass the Microsoft Quantum topological gap protocol (TGP). They have provided no parameters or data for this simulation (despite request). Regardless, in this reply we demonstrate that the trivial bulk gap reopening mechanism outlined in Hess et al., in combination with trivial ZBPs, passes the TGP and therefore can result in TGP false positives.
Many journals post accepted articles online before they are formally published in an issue. Early citation impact evidence for these articles could be helpful for timely research evaluation and to identify potentially important articles that quickly attract many citations. This article investigates whether Microsoft Academic can help with this task. For over 65,000 Scopus in-press articles from 2016 and 2017 across 26 fields, Microsoft Academic found 2-5 times as many citations as Scopus, depending on year and field. From manual checks of 1,122 Microsoft Academic citations not found in Scopus, Microsoft Academic's citation indexing was faster but not much wider than Scopus for journals. It achieved this by associating citations to preprints with their subsequent in-press versions and by extracting citations from in-press articles. In some fields its coverage of scholarly digital libraries, such as arXiv.org, was also an advantage. Thus, Microsoft Academic seems to be a more comprehensive automatic source of citation counts for in-press articles than Scopus.
Reliability is extremely important for large-scale cloud systems like Microsoft 365. Cloud failures such as disk failure, node failure, etc. threaten service reliability, resulting in online service interruptions and economic loss. Existing works focus on predicting cloud failures and proactively taking action before failures happen. However, they suffer from poor data quality like data missing in model training and prediction, which limits the performance. In this paper, we focus on enhancing data quality through data imputation by the proposed Diffusion+, a sample-efficient diffusion model, to impute the missing data efficiently based on the observed data. Our experiments and application practice show that our model contributes to improving the performance of the downstream failure prediction task.
Simulation, machine learning, and data analysis require a wide range of software which can be dependent upon specific operating systems, such as Microsoft Windows. Running this software interactively on massively parallel supercomputers can present many challenges. Traditional methods of scaling Microsoft Windows applications to run on thousands of processors have typically relied on heavyweight virtual machines that can be inefficient and slow to launch on modern manycore processors. This paper describes a unique approach using the Lincoln Laboratory LLMapReduce technology in combination with the Wine Windows compatibility layer to rapidly and simultaneously launch and run Microsoft Windows applications on thousands of cores on a supercomputer. Specifically, this work demonstrates launching 16,000 Microsoft Windows applications in 5 minutes running on 16,000 processor cores. This capability significantly broadens the range of applications that can be run at large scale on a supercomputer.
Microsoft Academic is a free academic search engine and citation index that is similar to Google Scholar but can be automatically queried. Its data is potentially useful for bibliometric analysis if it is possible to search effectively for individual journal articles. This article compares different methods to find journal articles in its index by searching for a combination of title, authors, publication year and journal name and uses the results for the widest published correlation analysis of Microsoft Academic citation counts for journal articles so far. Based on 126,312 articles from 323 Scopus subfields in 2012, the optimal strategy to find articles with DOIs is to search for them by title and filter out those with incorrect DOIs. This finds 90% of journal articles. For articles without DOIs, the optimal strategy is to search for them by title and then filter out matches with dissimilar metadata. This finds 89% of journal articles, with an additional 1% incorrect matches. The remaining articles seem to be mainly not indexed by Microsoft Academic or indexed with a different language version of their title. From the matches, Scopus citation counts and Microsoft Academic counts
In order to assess Microsoft Academic as a useful data source for evaluative bibliometrics it is crucial to know, if citation counts from Microsoft Academic could be used in common normalization procedures and whether the normalized scores agree with the scores calculated on the basis of established databases. To this end, we calculate the field-normalized citation scores of the publications of a computer science institute based on Microsoft Academic and the Web of Science and estimate the statistical concordance of the scores. Our results suggest that field-normalized citation scores can be calculated with Microsoft Academic and that these scores are in good agreement with the corresponding scores from the Web of Science.
There are Top three vendors in the ERP market: SAP, Oracle Net Suite and Microsoft dynamics 365 leading the Global ERP market.While analyzing the ERP selection and implementation trends, it is critical that any organization looking to implement an ERP system assesses the vendors through the lens of its own organization's specific requirements. When choosing the right ERP, a few things must be taken into consideration like the Time Budget and resources. The research paper analyses each phase and compares the methodologies of SAP, Oracle Net Suite and Microsoft Dynamics 365 and suggests the best methodologies to be practiced for any ERP projects. Like a poorly planned trip, if you don't have an effective methodology, you can expect a negative impact on your implementation, solution quality, and business satisfaction. Wrong choice of methodology may lead to poor decision-making, best practices may not be followed, and teams may be disjointed in the implementation, which can cause delays. Choosing the right ERP methodology is the key.Methodology is the lifeline for successful project implementation.
Modern analytical workloads are highly heterogeneous and massively complex, making generic query optimizers untenable for many customers and scenarios. As a result, it is important to specialize these optimizers to instances of the workloads. In this paper, we continue a recent line of work in steering a query optimizer towards better plans for a given workload, and make major strides in pushing previous research ideas to production deployment. Along the way we solve several operational challenges including, making steering actions more manageable, keeping the costs of steering within budget, and avoiding unexpected performance regressions in production. Our resulting system, QQ-advisor, essentially externalizes the query planner to a massive offline pipeline for better exploration and specialization. We discuss various aspects of our design and show detailed results over production SCOPE workloads at Microsoft, where the system is currently enabled by default.
Microsoft Academic is a free citation index that allows large scale data collection. This combination makes it useful for scientometric research. Previous studies have found that its citation counts tend to be slightly larger than those of Scopus but smaller than Google Scholar, with disciplinary variations. This study reports the largest and most systematic analysis so far, of 172,752 articles in 29 large journals chosen from different specialisms. From Scopus citation counts, Microsoft Academic citation counts and Mendeley reader counts for articles published 2007-2017, Microsoft Academic found a slightly more (6%) citations than Scopus overall and especially for the current year (51%). It found fewer citations than Mendeley readers overall (59%), and only 7% as many for the current year. Differences between journals were probably due to field preprint sharing cultures or journal policies rather than broad disciplinary differences.
The goal of this working paper is to summarize the main empirical evidences provided by the scientific community as regards the comparison between the two main citation based academic search engines: Google Scholar and Microsoft Academic Search, paying special attention to the following issues: coverage, correlations between journal rankings, and usage of these academic search engines. Additionally, selfelaborated data is offered, which are intended to provide current evidence about the popularity of these tools on the Web, by measuring the number of rich files PDF, PPT and DOC in which these tools are mentioned, the amount of external links that both products receive, and the search queries frequency from Google Trends. The poor results obtained by MAS led us to an unexpected and unnoticed discovery: Microsoft Academic Search is outdated since 2013. Therefore, the second part of the working paper aims at advancing some data demonstrating this lack of update. For this purpose we gathered the number of total records indexed by Microsoft Academic Search since 2000. The data shows an abrupt drop in the number of documents indexed from 2,346,228 in 2010 to 8,147 in 2013 and 802 in 2014
Scholarly knowledge graphs are valuable sources of information in several research fields. Despite the number of existing datasets related to publications and researchers, resource quality, coverage and accessibility are still limited. This article presents the Enhanced Microsoft Academic Knowledge Graph, a large dataset of information about scientific publications and involved entities, and the methods developed to build it. Data includes geographical information, researchers' collaborative networks and movements between institutions, academic-related metrics, and linguistic features. The dataset merges information from several data sources and has high temporal and spatial 7 coverage, allowing several use cases.