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            	{"first" : "Ralf",	"last" : "Diestelkämper"},
            	{"first" : "Boris",	"last" : "Glavic"},
            	{"first" : "Melanie",	"last" : "Herschel"},
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         "label" : "Provenance in DISC Systems: Reducing Space Overhead at Runtime",
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         "date" : "2020-03-26 09:59:23",
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         "year": "2017", 
         "url": "https://dl.acm.org/doi/abs/10.5555/3183865.3183883", 
         
         "author": [ 
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            	{"first" : "Melanie",	"last" : "Herschel"},
            	{"first" : "Priyanka",	"last" : "Jadhav"}
         ],
         "pages": "1-13","abstract": "Data intensive scalable computing (DISC) systems, such asApache  Hadoop  or  Spark,  allow  to  process  large  amountsof heterogenous data. For varying provenance applications,emerging provenance solutions for DISC systems track allsource data items through each processing step, imposing ahigh space and time overhead during program execution.We introduce a provenance collection approach that re-duces the space overhead at runtime by sampling the inputdata based on the definition of equivalence classes. A pre-liminary empirical evaluation shows that this approach al-lows to satisfy many use cases of provenance applications indebugging and data exploration, indicating that provenancecollection  for  a  fraction  of  the  input  data  items  often  suf-fices for selected provenance applications. When additionalprovenance is required, we further outline a method to col-lect provenance at query time, reusing, when possible, par-tial provenance already collected during program execution",
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         "label" : "A Survey on Provenance - What for? What form? What from?",
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         "volume": "26","pages": "881-906","abstract": "Provenance refers to any information describing the production process of an end product, which can be anything from a piece of digital data to a physical object. While this survey focuses on the former type of end product, this definition still leaves room for many different interpretations of and approaches to provenance. These are typically motivated by different application domains for provenance (e.g., accountability, reproducibility, process debugging) and varying technical requirements such as runtime, scalability, or privacy. As a result, we observe a wide variety of provenance types and provenance-generating methods. This survey provides an overview of the research field of provenance, focusing on what provenance is used for (what for?), what types of provenance have been defined and captured for the different applications (what form?), and which resources and system requirements impact the choice of deploying a particular provenance solution (what from?). For each of these three key questions, we provide a classification and review the state of the art for each class. We conclude with a summary and possible future research challenges.",
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