Data Structures using C provides its readers a thorough understanding of data structures in a simple, interesting, and illustrative manner. Appropriate examples, diagrams, and tables make the book extremely student-friendly. It meets the requirements of students in various courses, at both undergraduate and postgraduate levels, including BTech, BE, BCA, BSc, PGDCA, MSc, and MCA.
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Data Structures Using C by Rohit KhuranaBook Summary:Data Structures using C provides its readers a thorough understanding of data structures in a simple, interesting, and illustrative manner. Appropriate examples, diagrams, and tables make the book extremely student-friendly. It meets the requirements of students in various courses, at both undergraduate and postgraduate levels, including BTech, BE, BCA, BSc, PGDCA, MSc, and MCA.
ABSTRACTDatabase systems have traditionally relied on empirical approaches and handcrafted rules that encode human intuitions or heuristics to store large-scale data and process user queries over them. These well-tuned empirical approaches and rules work well for the general-purpose case, but are seldom optimal for any actual application because they are not tailored for the specific application properties (e.g., user workload patterns). Further, they fail to consider the complex interaction with the environment running the database systems (e.g., hardware and operating system). In this talk, I will show how we leverage machine learning to instance-optimize the performance of the query execution engine, the heart of any database system, using efficient learned models, data structures, and algorithms for three essential operations: query scheduling, hashing, and join processing. RELATED PAPERS1. Learned query scheduling: 2. Learned hashing: 3. Learned in-memory joins:
BIOIbrahim Sabek is a postdoctoral associate at the MIT Data Systems Group and an NSF/CRA Computing Innovation Fellow, working with Tim Kraska and Michael Cafarella. Before that, he completed his Ph.D. at the University of Minnesota, Twin Cities, under the supervision of Mohamed Mokbel. Ibrahim is interested in building the next generation of machine learning-empowered data management, processing, and analysis systems. His research focuses on deeply understanding machine learning and systems techniques, resulting in entirely new designs, algorithms, and data structures for data-intensive systems and applications. His research areas broadly include instance-optimized database systems, scalable knowledge base construction, big spatial data management and analysis, and data processing in containerization environments. For more information about Ibrahim, please check his website:
TITLETensors: An abstraction for data processing ABSTRACTDeep Learning (DL) has created a growing demand for simpler ways to develop complex models and efficient ways to execute them. Thus, a significant effort has gone into frameworks like PyTorch or TensorFlow to support a variety of DL models and run efficiently and seamlessly over heterogeneous and distributed hardware. Since these frameworks will continue improving given the predominance of DL workloads, it is natural to ask what else can be done with them. This is not a trivial question since these frameworks are based on the efficient implementation of tensors, which are well adapted to DL but, in principle, to nothing else. In this paper, we explore to what extent Tensor Computation Runtimes (TCRs) can support non-ML data processing applications, so that other use cases can take advantage of the investments made on TCRs. In particular, we are interested in graph processing and relational operators, two use cases very different from ML, in high demand, and complement quite well what TCRs can do today. Building on Hummingbird, a recent platform converting traditional machine learning algorithms to tensor computations, we explore how to map selected graph processing and relational operator algorithms into tensor computations. Our vision is supported by the results: our code often outperforms custom-built C++ and CUDA kernels, while massively reducing the development effort, taking advantage of the cross-platform compilation capabilities of TCRs. RELATED PAPERS:1) He, Dong, et al. "Query processing on tensor computation runtimes." arXiv preprint arXiv:2203.01877 (2022) ( ).2) Müller, Ingo, et al. "The collection Virtual Machine: an abstraction for multi-frontend multi-backend data analysis." Proceedings of the 16th International Workshop on Data Management on New Hardware. 2020 ( ).3) Koutsoukos, Dimitrios, et al. "Modularis: modular relational analytics over heterogeneous distributed platforms." arXiv preprint arXiv:2004.03488 (2020) ( ).BIO Dimitris Koutsoukos is a PhD student in the Systems Group at ETH Zurich, supervised by Gustavo Alonso and Ana Klimovic. He is broadly interested in database systems and more specifically on how we can execute database workloads on heterogeneous architectures (RDMA, serverless, NVM) and adapt the next generation of DBMSs in the cloud. More recently, he is looking into using compiler infrastructure like MLIR for databases and how we can optimize data layouts for serverless databases. He holds a MSc in Data Science from ETH Zurich. Before coming to ETH, he obtained my undergraduate degree in Electrical and Computer Engineering from the National Technical University of Athens. In the past, he has worked for Meta, Orfium, Maastricht University and he has collaborated with Microsoft Gray Systems Lab.
BIOPhilippe Cudre-Mauroux is a Full Professor and the Director of the eXascaleInfolab at the University of Fribourg in Switzerland. He received his Ph.D.from the Swiss Federal Institute of Technology EPFL, where he won both theDoctorate Award and the EPFL Press Mention in 2007. Before joining theUniversity of Fribourg, he worked on information management infrastructures atIBM Watson (NY), Microsoft Research Asia and Silicon Valley, and MIT. Herecently won the Verisign Internet Infrastructures Award, a Swiss NationalCenter in Research award, a Google Faculty Research Award, as well as a 2million Euro grant from the European Research Council. His research interestsare in next-generation, Big Data management infrastructures for non-relationaldata and AI. Webpage:
BIOSudeepa Roy is an Associate Professor of Computer Science at Duke University. She works broadly in data management, with a focus on foundational aspects of big data analysis, including causality and explanations, data provenance, uncertain data, data repair, query optimization, and database theory. Prior to joining Duke in 2015, she did a postdoc at the University of Washington, and obtained her Ph.D. from the University of Pennsylvania. She is a recipient of an NSF CAREER Award, the Very Large Databases (VLDB) Early Career Research Contributions Award, and a Google Ph.D. fellowship in structured data. She co-directs the Almost Matching Exactly (AME) lab for interpretable causal inference at Duke ( -matching-exactly.github.io/).
ABSTRACTData analytics skills have become anindispensable part of education and modern workforce, relational queries, theessential tools of the trade for manipulating and analyzing structured data,are still notoriously challenging to learn and debug even for people withconsiderable experience in programming. In this talk, I will introduce works inthe HNRQ project (Helping Novices learn and debug Relational Queries), whichprovide users with small counterexample database instances pointing out errorsin the user query and allow users to trace how the query executes, based ontheoretical foundations for data provenance and incomplete data. The practicalsystem for debugging database queries was successfully deployed at DukeUniversity and used by more than 1000 students in introductory databasecourses.
TITLESemantic Advancement of Data for Data AnalyticsABSTRACTIn this work, we use semantic knowledge sources, such as cross-domain knowledge graphs (KGs) and domain-specific ontologies, to enrich structured data for various anaytics applications. By enriching our understanding of the underlying data with semantics brought in from external ontologies and KGs, we can better interpret the data as well as the queries to answer more questions, provide more complete answers, and deal with entity disambiguation. To semantically enrich the data with external knowledge sources, we need to find the correspondences between the structured data and the entities in the cross-domain KGs and/or the domain-specific ontologies. In this presentation, we break this problem into several steps, and provide solutions for each step. We showcase the practical value of semantic enrichment of data using our proposed techniques in entity disambiguation, natural language querying and conversational interfaces to data, query relaxation, with promising initial results.RELATED PAPERS
BIOFatma Özcan is a Principal Engineer at Systems Research@Google. Before that, she was a Distinguished Research Staff Member and a senior manager at IBM Almaden Research Center. Her current research focuses on platforms and infra-structure for large-scale data analysis, query processing and optimization of semi-structured data, and democratizing analytics via NLQ and conversational interfaces to data. Dr Özcan got her PhD degree in computer science from University of Maryland, College Park, and her BSc degree in computer engineering from METU, Ankara. She has over 21 years of experience in industrial research, and has delivered core technologies into IBM products. She has been a contributor to various SQL standards, including SQL/XML, SQL/JSON and SQL/PTF. She is the co-author of the book "Heterogeneous Agent Systems", and co-author of several conference papers and patents. She received the VLDB Women in Database Research Award in 2022. She is an ACM Distinguished Member, and the vice chair of ACM SIGMOD. She has been serving on the board of directors of CRA (ComputingResearch Association) since 2020, and is a steering committee member of the CRA-Industry. 2ff7e9595c
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